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All you need is love… or what?

Language is essentially always present in groups of modern humans. Even in the exceptional groups that for some reason are formed without language, language will invariably emerge in short order. Examples of language emergence in recent times include deaf communities in e.g. Nicaragua and Israel. Such newly-formed languages converge within a few generations towards the same general form and features as mainstream human languages.

Language is essentially never present in groups of non-human primates. Even in the exceptional groups that are heavily exposed to language and explicitly trained in language use, progress in language acquisition is invariably modest at best. Language never emerges spontaneously in non-human groups.

What’s special with humans? It is sometimes argued that “all you need is merge” (e.g. Berwick 2007), that a small genetic change provided a language-ready brain and the rest is history. This saltational view of language evolution is wrong for many reasons (e.g. Tallerman 2014), but I would add here another one.

A language-ready brain is not an all-or-nothing affair, nor is it sufficient for language emergence. The results of language training in apes are modest, but not nil. Apes do learn to connect symbols with referents and use them communicatively. One may quibble about whether to call this “language”, and it is far from full human language, notably lacking in syntax. But it does show the presence of some language-relevant abilities in apes, and it is a functional communication tool at some protolinguistic level.

But if ape brains are protolanguage-ready, why doesn’t protolanguage emerge in the wild among apes, as it does among humans? Clearly, some extra-linguistic key factor is lacking. A language-ready brain is not all you need for language emergence. In a group of hypothetical creatures with a human language faculty (narrow sense) but otherwise ape-like in psychology and behavior, language would not emerge.

Human prosociality and shared intentionality are likely key ingredients in language emergence (e.g. Tomasello 2010), but are not the whole story. In this talk, I will explore the minimal extra-linguistic requirements for protolanguage emergence to get off the ground in protohumans.

References:

Berwick, R C (2011) All you Need is Merge: Biology, Computation, and Language from the Bottom-up.  In di Sciullo & Boeckx The Biolinguistic Enterprise OUP.

Tallerman M. (2014) No syntax saltation in language evolution. Language Sciences 46, 207-219.

Tomasello, M (2010) Origins of human communication. MIT Press.

1. Introduction

A major component in the evolution of language is the evolution of the human language capacity, whatever biological endowments humans have that make us language-ready. But the language capacity is not well understood and is difficult to study directly. Clues may come from biases displayed by humans in language acquisition and language change. Even weak underlying biases can lead to strong patterns in the resulting languages (Smith, 2011). Biases can be studied at the individual level in learning experiments (e.g. Culbertson, 2012, Tamariz et al., of natural languages (e.g. Dediu & Ladd, 2007). Biases can be seen either in the synchronic patterns of language features today, or in the diachronic patterns of transition probabilities between features as languages culturally evolve (e.g. Dunn et al, 2011).

Patterns that reveal biases may be found in any aspect of language, e.g. syntax, morphology, phonology, or lexicon, and may be subtle enough to be discernible only in large samples of languages. This work is an exploratory study across the widest possible set of languages, combining typological, phonological, lexical and phylogenetic data on a significant fraction of the languages of the world, with the goal of mapping any biases that may be present. Both synchronic and diachronic patterns are studied, with the emphasis on the latter.

2. Data set

The following data sources are used:

•Phylogeny and geography: Ethnologue (Simons & Fennig 2017); ~7,500 languages.

• Phonological inventories: PHOIBLE (Moran & McCloy & Wright 2014); ~1,800 languages.

• Typology: WALS (Dryer & Haspelmath 2013); ~2,500 languages.

• Lexicon (Swadesh lists): Rosetta Project Digital Language Archive (2009); ~1,300 languages.

All four types of data are available for ~300 languages. At least three types are available for ~1,600 languages from 132 different stocks. In order to keep the data set as homogeneous as possible, each type of data has been imported from a single source only. Languages are identified between data sources by their ISO codes. 3. Methods

The language phylogeny from Ethnologue is taken as given in the analysis. For the synchronic analysis, the phylogeny is taken into account in the character statistics by down-weighting multiple “hits” in the same family, in order to control for phylogenetic bias and lineage-specific patterns. Geographic data is also available to control for areal effects. Cross-correlations between different types of characters are analysed for possible patterns. For the diachronic analysis, the phylogeny together with modern-day character data are used to infer both ancestral character states up the language tree for phonological and typological characters, and transitional probabilities between states (including the probability of characters appearing and disappearing), in a bootstrapping process. 4. Some preliminary results

Well-known typological patterns are reproduced. But correlations between features are observed that go beyond those normally discussed in typology, or those observed by Dunn et al (2011). Interestingly, there are also some modest cross-correlations between grammatical features and phonemes. For example, the presence of aspirated consonants and nasal vowels correlates with certain word- order features, even after controlling for phylogeny. In the diachronic analysis, there are hints of patterns beyond the obvious one that transition probabilities into common features are larger, but much work remains to be done in the interpretation of these patterns.

It is commonly assumed in evolutionary linguistics that language evolved for communication.But much recent work in biolinguistics, e.g. Chomsky (2010), proposes instead that languageevolved for purely internal use, as a cognitive tool, with no externalization until at a later stagein language evolution.How well supported is really our general assumption of communicative language origins? Doesit make sense to have instead an early stage with internal language only? I will review the argumentsinvoked in favor of the incommunicado hypothesis, and critically evaluate their strength.

Why do humans have language at all and how did we become language users? These are central questions in language evolution, but no general consensus exists on the answers, nor even on what methods to use to find answers. This is a complex topic that requires input from many disciplines, including, but not limited to, linguistics, evolutionary biology, palaeoanthropology, neurobiology, archaeology, cognitive science, and primatology. Nobody is an expert in all these areas, and experts in one area sometimes overlook needed input from other areas. Consensus does not even exist among linguists on what language is—opinions range from the physical speech acts themselves to language as an abstract social communication system to language as computational machinery in the individual and to language as an innate species-defining, genetically encoded capacity of humans. These different views of language imply very different evolutionary explanations. At the same time, all of these perspectives have some validity; the speech acts do occur, language use does take place in a social context, the individual language user does somehow produce and parse sentences, and human babies are born with a predisposition for language learning that ape babies lack. The disagreements are mainly a matter of emphasis, namely which aspects are regarded as of primary interest, requiring explanation. The preeminent linguist of the early 20th century, Ferdinand de Saussure, focused on the first two perspectives with his distinction between parole (speech acts) and langue (the social system). The preeminent linguist of the late 20th century, Noam Chomsky, focuses instead on the latter two, especially the computational machinery, and he regards the first two as not worthy of a linguist’s attention. But neither focus is adequate on its own; a viable theory of language evolution must be able to explain all aspects of language, notably both the evolution of the language capacity that resides in each human brain and the evolution of the human social context in which language is used. No generally accepted theory exists today. Instead of a single accepted theory, the field of language evolution is awash with a multitude of different models, scenarios, and hypotheses about how things might have happened. To make matters worse, there is something of a paradigm split in the study of language origins. The split is largely along the line between Saussure and Chomsky mentioned above. To put it simply, those researchers who use the label “biolinguistics” try to explain the origin of Chomsky’s computational machinery (see Biolinguistics) whereas most work on language evolution is concerned with explaining the origins of Saussure’s langue, language as a social system; the latter is here called “mainstream evolutionary linguistics.” Language evolution is not, however, about the origin of individual languages (English, Chinese, etc.). Sometimes “language evolution” is used to refer to diachronic language change in recent times, as studied by historical linguists, and an evolutionary perspective can indeed be fruitful in this area. But this article does not cover that kind of language evolution, except peripherally in Cultural Evolution.

Chomsky (e.g. 2010) and others regard unlimited Merge as the defining feature of language, that cannot evolve gradually. The neural implementation of Merge is not well understood (Rizzi 2012, Zaccarella et al 2017), but must involve something functionally equivalent to pointers in working memory. Every Merge requires two pointers, and full syntactic trees may require dozens. Other syntactic paradigms also need pointers.

Humans do hierarchies in general better than chimpanzees. Any hierarchical thinking requires nested pointers in working memory, but they are neurologically expensive and degrade with depth (Crawford et al. 2016). Humans have larger working-memory capacity than chimpanzees, which has been proposed as key to human cognitive evolution (Read 2008, Coolidge & Wynn, 2005). Gradual evolutionary growth of pointer capacity will allow gradually increasing syntactic complexity, without saltations in the underlying computational machinery. Both depth degradation and pointer capacity naturally limit Merge even in modern humans, consistent with corpus data (e.g. Karlsson 2010).

Chomsky, Noam. (2010). Some simple evo devo theses: how true might they be for language? In Richard K Larson, Viviane Déprez, & Hiroko Yamakido (Eds.), The Evolution of Human Language. Biolinguistic Perspectives. Cambridge: Cambridge University Press.

Coolidge, Frederick L & Wynn, Thomas (2005) Working memory, its executive functions, and the emergence of modern thinking. Cambridge Archaeological Journal 15:5-26.

Crawford, Eric & Gingerich, Matthew & Eliasmith, Chris (2016) Biologically plausible, human-scale knowledge representation. Cognitive Science 40:782-821.

Karlsson, Fred (2010) Syntactic recursion and iteration. In Harry van der Hulst, ed., Recursion and Human Language. Berlin/New York: Mouton de Gruyter,

Read, Dwight W (2008) Working memory: A cognitive limit to non-human primate recursive thinking prior to hominid evolution. Evolutionary Psychology 6:676-714.

Rizzi, Luigi (2012) Core linguistic computations: How are they expressed in the mind/brain? Journal of Neurolinguistics 25:489-499.

Zaccarella et al (2017) Building by syntax: the neural basis of minimal linguistic structures. Cerebral Cortex 27:411-421.

When we talk about protolanguage, it is typically in the singular. The name of this conference series is Exhibit A here, but also e.g. the authoritative Oxford Handbook of Language Evolution talks about “the protolanguage” (Tallerman, 2012, my emphasis). This leads us to imagine a single protolanguage being spoken (or signed or mimed or whatever) at some point in prehistory. At most we imagine multiple successive stages of more and more advanced protolanguages (e.g. Jackendoff & Wittenberg, 2014, Author, submitted a). 

But rarely has the likely diversity of protolanguages been considered. A literature search for “protolanguages” in the plural mainly returns results referring to the proto-forms ancestral to modern language families (proto-Indoeuropean, etc.); see Robbeets et al. (2020) for an example. There are rare exceptions referring to plural protolanguages in the sense used at this conference (e,g, Donald, 1999, Dowman, 2008) but they still do not have diversity in focus.

Is there any reason to believe that linguistic diversity in the world was any less in prehistory than it is today? The answer to that question depends on whether the forces driving linguistic diversification were operative already at the protolinguistic stage. If two groups speaking the same language get separated, their dialects will gradually drift apart over time until mutual intelligibility is lost, the linguistic equivalent of genetic drift in biology; this must have been operative for as long as we had conventionalized language at all. Differential language contact can also cause language change, with different subgroups accumulating different loanwords etc. In this way, diversity begets more diversity, and this would have been operative as soon as some diversity was present. Finally, if language is used as a marker of group identity, a group may intentionally steer their way of speaking away from the neighbors’. This requires a level of meta-linguistic awareness that need not have been present from the beginning. 

But even without the latter force, linguistic drift and language contact would have been quite sufficient to drive diversity among protolanguages. And even in the absence of synchronic diversity, linguistic drift would create diachronic diversity: over a millennium or so, a modern language changes beyond intelligibility, and across 10,000 years it changes beyond recognition even at the family level. Even if we assume for the sake of the argument that protolanguage was a single lineage, every 1,000 years what is spoken deserves to be called a new language.

A single lineage is, however, extremely unlikely. From the dawn of language, people have spread out over multiple continents, far out of touch with each other. Protolanguages must have diverged. But populations were sparse, estimates for world population at relevant times are typically below one million, sometimes well below (e.g. Huff et al. 2010), living in small tribes. From such numbers, a rough order-of-magnitude estimate of the total number of protolanguages ever spoken can be calculated (Author, submitted b). With any reasonable assumptions, the number is huge, much larger than current linguistic diversity.

Vad heter du? Den frågan har du fått många gånger.

Men varför heter du? Den frågan har du kanske inte tänkt på så ofta. Hur kommer det sig att du över huvud taget har ett namn, och varför ser det ut som det gör? Det vill Sverker Johansson berätta om i denna underhållande och tanke­väckande bok. Med blick för såväl de stora utvecklings­linjerna som för de kuriösa exemplen rör han sig mellan alla tider och platser för att fånga in namntraditionernas mångfald.

Beroende på var vi hamnar i tid och rum kan ett för­namn bestämmas utifrån veckodag, förfäder, vilka onda andar som ska luras eller vad modern ser när barnet föds. Förutom förnamn kan vi ha efternamn – men också föräld­ranamn, gårdsnamn, bynamn, barnnamn, kastnamn, mel­lannamn, generationsnamn eller tillnamn baserade på allt från lyten via yrken till tv-program. Visste du till exempel att Caligula betyder barnstövlar, att John är det vanligaste förnamnet på Wikipedia, att delfiner har namnmelodier och att Mao Zedongs mellannamn Ze kommer från en dikt som hans släkt i fjorton generationer hämtade namn ur?

Mycket pekar på att vi bär namn inte så mycket för att kunna tilltalas som för att kunna omtalas…

Neanderthal language abilities cannot be directly observed, but indirect evidence is available in their anatomy, archeology, and DNA. Neanderthal anatomy shows possible speech adaptations, and their archeology contains enough indicators of behavioral modernity, including symbols and ornaments, to conclude that their minds could handle symbolic communication. Neanderthal DNA, finally, indicates both that they possessed some of the language-relevant genes found in modern humans and that they could and did have children with modern humans. From the consilience of evidence from anatomy, archeology, and DNA, one can conclude that some language abilities, if not necessarily full modern syntactic language, were present in Neanderthals.

Midwives and the birth of language

Sverker Johansson

Dalarna University

Sweden

Language is a paradox in signal evolution theory. Cheap signals can evolve only between beings who trust each other, or who have totally aligned interests. But totally aligned interests is a utopia, and our knuckle-walking relatives generally do not trust each other? How and when did human trust evolve? This will set a baseline for language evolution – except that trust does not fossilize any more than language does.

What fossil and archeological proxies for trust can be found? Trust is a social matter, but even proxies for sociality are not trivial to identify (Johansson 2014). Probably the best proxy for human trust was identified by Hrdy (2011), in proposing cooperative breeding as a key innovation in human evolution. Ape mothers are paranoid about their babies, for good reason, and will not let anybody assist them. But in all human cultures, family and friends will routinely cooperate and help a mother with her children, and experienced women will serve as midwives in labor. This makes a huge difference for human fertility, our reproductive rate “in the wild” is roughly double that of other apes. This provides the Darwinian payoff needed to overcome the threshold of mutual mistrust, and paves the way for cheap linguistic communication.

Midwife assistance in labor may facilitate language evolution also in another way, as it eases obstetric constraints on brain size.

I will review here the fossil and archeological evidence indicating the presence among our ancestors of the modern human pattern of cooperative breeding and labor assistance. The conclusion is that the first midwife most likely was a Homo erectus… and maybe some millennia later a young erectus first cried “mama”, when left in the care of an auntie.

Hrdy, Sarah Blaffer (2011) Mothers and Others. The Evolutionary Origins of Mutual Understanding. Cambridge, MA.: Harvard University Press.

Johansson, Sverker (2014) How can a social theory of language evolution be grounded in evidence? In Lewis, Jerome, Daniel Dor & Chris Knight (eds.) Social Origins of Language. Oxford: Oxford University Press.

Ackermann et al. treat both genetic and paleoanthropological data too superficially to support their conclusions. The case of FOXP2 and Neanderthals is a prime example, which I will comment on in some detail; the issues are much more complex than they appear in Ackermann et al.

Chomsky (e.g. 2010) and others regard unlimited Merge as the defining feature of language, that cannot evolve gradually. The neural implementation of Merge is not well understood (Rizzi 2012, Zaccarella et al 2017), but must involve something functionally equivalent to pointers in working memory. Every Merge requires two pointers, and full syntactic trees may require dozens. Other syntactic paradigms also need pointers.

Humans do hierarchies in general better than chimpanzees. Any hierarchical thinking requires nested pointers in working memory, but they are neurologically expensive and degrade with depth (Crawford et al. 2016). Humans have larger working-memory capacity than chimpanzees, which has been proposed as key to human cognitive evolution (Read 2008, Coolidge & Wynn, 2005). Gradual evolutionary growth of pointer capacity will allow gradually increasing syntactic complexity, without saltations in the underlying computational machinery. Both depth degradation and pointer capacity naturally limit Merge even in modern humans, consistent with corpus data (e.g. Karlsson 2010).

Identifying possible stages of protolanguage critically depends on the underlying nature of language. Theories of language differ in evolvability, and in whether they permit protolanguage stages. In this presentation, I will study the protolanguage potential and evolva­bility of Construction Grammar. Postulating that CG is a biologically real description of language, its evolvability through a sequence of intermediate protolanguages is investigated.

Hur blev människan med språk? Var, när och varför bör­jade vi tala? Det är en av historiens stora gåtor. Än är vi långt ifrån en lösning, men med hjälp av så olika vetenska­per som arkeologi, neurologi, lingvistik och biologi kan vi numera dra några slutsatser, avfärda vissa äldre hypoteser och uppställa nya frågor.

Med entusiasm och sakkunskap lotsar Sverker Johansson läsaren genom en djungel av ledtrådar och teorier. Sök­andet efter språkets ursprung börjar många miljoner år tillbaka i tiden, då dagens apor och människor gick skilda evolutionära vägar. Det slutar vid den punkt dit det går att härleda förlagorna till de språk som talas i dag, det vill säga för omkring fem tusen år sedan. Däremellan får vi stifta bekantskap med Homo erectus och neandertalare, med Darwin och Chomsky, med delfiner och näktergalar, med syntax och interjektioner.

Men hela tiden tycks spåren leda tillbaka den omväl­vande period för omkring en och en halv miljon år sedan, då våra förfäder i Afrika ställdes inför nya situationer och alltmer började skilja sig från övriga djur och där språket av allt att döma tycks ha spelat en nyckelroll.

“Who did the first speaker talk with?” is a classic chicken-and-egg argument against the Darwinian evolution of language, still occasionally heard as an argument for non-communicative language origins. Various language-origins scenarios solve the problem in different ways. But I will argue that ancestral ostensive-inferential communication provides a general solution, insensitive to scenario details.

Apes use communicative gestures intentionally and likely ostensively (Moore 2016; pace Scott-Phillips 2015), and interpret each other’s gestures accordingly. Such proto-ostensive-inferential abilities in proto-humans will handle new expressive abilities in “speakers” without requiring simultaneous changes in “listeners”, thus relaxing chicken-and-egg constraints on language evolution.

Dendrophilia (Fitch 2014), if evolved for non-linguistic hierarchic-processing purposes, may similarly help bootstrapping the final step from proto-language to modern language.

Chicken-and-egg is a problem for language evolution only if communication is a coding-decoding process. Ostensive-inferential communication can handle substantial mismatches between speakers and hearers, separating chicken from eggs.

Fitch, W Tecumseh (2014) Toward a computational framework for cognitive biology: unifying approaches from cognitive neuroscience and comparative cognition. Phys Life Reviews 11:329-364

Moore, Richard (2016) Meaning and ostension in great ape gestural communication. Animal Cognition 19:223-231.

Scott-Phillips, T C (2015) Meaning in animal and human communication. Animal Cognition 18:801-805.

Languages change over time, due to various processes that likely have been operative since the dawn of language. But our understanding of the relative importance of different processes in the distant past remains limited. Methods for reconstructing language change are hampered by shortage of training data.

Simulating language change in software can help, testing processes and producing simulated language data as input for reconstruction tests. In simulation, the processes are known and controllable, and the true diversification path is known. Tuning process strength in simulation until the results resemble real language diversity may inform theories of language dynamics. 

But simulated data will only be helpful if the simulation reproduces relevant aspects of reality closely enough. Several items in List (2019) Open problems in computational linguistics concern simulation issues. Extant simulations are mainly of two types: 

• Detailed short-term simulations of within-language dynamics, often agent-based (e.g. Nolfi & Mirolli, 2010).
• Macro-scale long-term simulations, but with linguistic and/or geographical details abstracted away (e.g. Wichmann, 2017; Kapur & Rogers, 2020).

Neither type covers the middle ground where within-language and between-language dynamics meet. This work aims to fill that gap, with a simulation that has sufficient linguistic, geographic and anthropological detail to produce realistic data, and sufficient scope to cover macro-scale dynamics over millennia.

The basic simulation unit is a speech community with typically 100-1000 speakers, speaking a common language. Their language has an explicit vocabulary with word-forms and meanings. Real languages from CLICS3 (Rzymski et al., 2019) are used as seed languages, which then evolve through regular sound change, word gain and loss, semantic shift, language contact, and areal effects. All processes are adjustable and can be disabled.

The geography of the real world is used, with topography from De Ferranti (2015), rivers from Kelso (2016) and climate/ecology from NASA (2016). Each speech community lives in a 50x50 km grid square, which may be shared with other communities up to a carrying capacity. Population may increase or decrease depending on food availability, and surplus population may migrate to greener pastures, forming a new community whose language then evolves independently. Travel depends on real terrain and available technology (innovations occur occasionally, starting from paleolithic level).

Simulation results are available as Swadesh matrices, or in formats suitable for automated reconstruction such as CLDF or NEXUS. True trees and true cognate sets are saved separately.

Software and sample output available at https://github.com/[ANONYMIZED]/LangChangeSimulator/tree/master 

De Ferranti, J. (2015) Viewfinder Panoramas Digital Elevation Model. http://www.viewfinderpanoramas.org/dem3.html 

Kapur, R & Rogers, P (2020) Modeling language evolution and feature dynamics in a realistic geographic environment. In Proceedings of the 28th International Conference on Computational Linguistics, Barcelona.

Kelso, N V (2016) Natural Earth Data. https://www.naturalearthdata.com/downloads/ 

List, Johann-Mattis (2019): Open problems in computational historical linguistics. Invited talk presented at the 24th International Conference of Historical Linguistics (2019-07-01/05, Canberra, Australian National University).

NASA (2016) NASA Earth Observations. https://neo.gsfc.nasa.gov/ 

Nolfi, S & Mirolli, M (2010) Evolution of Communication and Language in Embodied Agents. Springer.

Rzymski, Christoph and Tresoldi, Tiago et al. 2019. The Database of Cross-Linguistic Colexifications, reproducible analysis of cross- linguistic polysemies. DOI: 10.1038/s41597-019-0341-x

Wichmann, S. (2017) Modeling language family expansions. Diachronica 34:1, 79-101.

The origin of language is a problem involving complex interactions between a number of different evolving systems. Language per se, regarded as a cultural/memetic entity, is one of the evolving systems, and its evolution is of major importance in the origin of modern human language. Possible structural parallels between language evolution and biological evolution are discussed. Genes, organisms, and species are key concepts in biology, and an understanding of the corresponding levels in language is needed for any fruitful linguistic application of theoretical tools from evolutionary biology. I identify candidate linguistic ’genes’, ’organisms’ and ’species’, and discuss implications for language evolution.

The study of Neanderthal cognition is difficult, because of the archaeological invisibility of cognition, and because of the methodological issues that arise both from that invisibility and from their being close to modern humans. Nevertheless, fair progress has been made in gathering relevant evidence. There is now good evidence that Neanderthals were cognitively sophisticated, displaying many of the cognitive traits that were traditionally regarded as proxies for modern human cognition, notably including language. It can neither be proven nor excluded that they were our cognitive equals, but they were close enough to us, biologically and cognitively, to interbreed successfully and leave a genetic legacy in our DNA. 

This chapter aims to describe how cultures have emerged in interactions among users of the multitude of online platforms that have become available over the past few decades. It discusses innovations regarding uses of representations to communicate identity, time, and space in social practices with technology, and how cybercultures are played out in theory and in practice. Cybercultures resemble cultures in the non-virtual world—but display significant differences regarding social rules, identity, and spatiotemporal issues. Case studies of three types of cybercultures in social media: information and knowledge building on Wikipedia, culture, and virtual world building on Second Life, and dating practices on online dating services, such as Tinder, will shed light on how cyberspace allows for developing both symbolic representations and social practices through computer-mediated communication (CMC), and how users are situated in the continuum virtual-real.

Most assessment of students is based on artificial assignments, done purely for assessment, only read by assessing teachers. Much has been written on “authentic assessment” (reviewed in Frey et al. 2012), meant to mimic “the real world” in some sense. But even this is commonly not actual real-world assignments that reach a wider audience than teachers.

Furthermore, in many educational contexts, teachers want to assess not just subject knowledge, but also e.g. writing skills, cooperative project-working skills, and skills in explaining the subject matter to others. These are non-trivial to assess either with traditional forms of assessment or with forms available in NGL contexts.

One tool for assessing cooperative writing skills in NGL contexts is Wiki technology for joint text production. This is the same technology used in Wikipedia, but in educational contexts dedicated Wiki installations are typically used, with mixed results (e.g. Bruns & Humphreys 2005, Judd et al. 2010, Guth 2007).

We have used Wikipedia itself for assessment in several courses in different subjects, from physics to literature, with fair success. Students are assigned the task of writing Wikipedia articles within the course topic.

Wikipedia assessment is suitable for courses with specific characteristics. In such courses it has multiple advantages:

• Authentic assessment, with student texts widely read by the general public, enhancing student motivation.
• Feedback from and enforced collaboration with both the Wikipedia community and fellow students.
• Straightforward tracking of individual student contributions in collaborative texts.
• No setup and maintenance of dedicated system.
• Valuable training in source criticism.
• Writing process…

Technical hurdles in Wikipedia writing are modest, but require some instruction. Copyright is an issue, making it legally difficult to force students to write for Wikipedia.

Frey, Schmitt, & Allen (2012), Defining Authentic Classroom Assessment. Practical Assessment, Research & Evaluation, Vol 17, No 2:14

Wikipedia is an online encyclopedia, written entirely by volunteers in 288 different languages. It is the 6^th most visited of all websites, and is both the largest and the most used encyclopedia of any kind. Both teachers and students regularly use Wikipedia as a tool, but are often unaware of its translanguaging potential.

Wikipedia itself is multilingual rather than translingual, but can nevertheless be a valuable resource in the translanguaging classroom, mainly because it contains linked quasi-parallel texts in many languages on almost any topic. We see at least three levels of translanguaging Wikipedia use:

• Source of knowledge, either in whatever language the student is most comfortable with or in a target language for language learners.
• Comparison between languages. Both linguistic, genre, selection and perspective differences in the presentation of the same topic in different languages can give rise to fruitful classroom discourse across and between languages. These translanguaging comparisons can be an eminent tool for the development of critical thinking skills in students.
• Translanguaging writing, where students add text to Wikipedia in multiple languages, using sources in one language to write in another. Either a Wikipedia article in one language can be created or extended using input from the same article in another language, or Wikipedia articles on the same topic in more than one language can be written concurrently.

The focus of our study is on the second point, comparison between languages. We have investigated the types of differences than can be observed between languages in articles on the same major topics, using both quantitative and qualitative methods.

This entry departs from promissory imaginaries about educational technology to show how postdigital research adopts a critical stance on futures. Futures are observed as both a research object and a methodological frame to understand an increasingly complex and technology-dense reality. Temporalities are identified as follows: futures in the present, near futures, far futures, and alternative futures. These situated time-spaces cater for different methodological approaches. Anticipatory methods are elaborated in relation to a near future, while far futures can be grasped through speculatively methods. Alternative futures are usually formulated in the literary vein of science fiction. The tendency in recent futures studies is to make explicit dimensions of ethics, care, and values in the crafting of educational futures.