Bibliographic record
Abstract
From a logical point of view, one would expect every negative element to contribute a negation in the semantics. This is the case for languages like Dutch: the combination of two negative elements (the negative marker “niet” and the negative indefinite “niemand”) in both (1-a) and (1-b) give rise to a double negation interpretation, resulting in a positive meaning. (1) a. Niemand rent niet. N-body NEG run. “Nobody doesn’t run” → “Everybody runs” b. Jan belt niet niemand Jan calls NEG n-body “Jan doesn’t call nobody” → “Jan calls somebody” However, this is not the case in all languages. For example, despite involving two negative elements (the negative marker “ne” and the negative indefinite “niko(ga)”), a Serbian sentence like (2-a) only contains one semantic negation. In other words, the two negative elements in both (2-a) and (2-b) do not each contribute a semantic negation, but rather convey a negative meaning together, yielding what is known as a negative concord interpretation. 2) a. Niko ne trci N-word NEG run “Nobody run” b. Milan nikoga ne zove. Milan n-body NEG calls “Milan calls nobody” Words like “niko”, which introduce negative force but participate in negative concord relations, are often referred to as “n-words” (Laka 1990), to differentiate them from words like Dutch “niemand” (which stand on their own). The contrast between (1) and (2) illustrates the existence of cross-linguistic variation wrt the interpretation of negative elements. A natural question to ask is what determines the distribution and behaviour of double negation (DN) and negative concord (NC) languages. That is, where does the difference between languages like Dutch and languages like Serbian come from. Accounts of the difference between DN and NC languages have often relied on the existence of other linguistic properties that correlate with the kinds of interpretations that sentences like (1)/(2) can get. One observation that has played an important role in current theories of negative concord was made by Jespersen in 1917. Jespersen noted that whether a language is double negation or negative concord correlates with the phonological and syntactic nature of the negative marker. That is, whether the negative marker is an adverb or a particle/affix (see Zanuttini 1997 for tests to tease these apart). More specifically, Jespersen argued that languages which have only a negative adverb always exhibit double negation, while languages which have only a negative particle/affix always exhibit negative concord. While this generalization, as it is, is too strong (there are negative concord languages that only have negative adverbs; e.g., Quebecois, Deprez 1997), it can still be accurately reformulated in an unidirectional way: a language may lack negative concord if and only if it has a negative adverb (and not a negative particle or affix) (Zeijlstra 2004). The Jespersen-Zeijlstra generalization is important for current theories of negation because it suggests that the contrast between DN and NC languages is partially due to a difference in the syntactic status of adverbs and particles, which would in turn have consequences on how they compose with negative indefinites. For instance, according to Z., only negative particles/affixes can license n-words, so only languages that have those should be able to have negative concord interpretations. The exact strength of this generalization, however, remains unclear: Jespersen’s original generalization had to be weakened as more typological data became available, leaving open the possibility that even a uni-directional interpretation may be too strong. In this project, we investigate the Jespersen-Zeijlstra generalization by testing whether learners are sensitive to the correlation between type of negative marker and being NC or DN. Are English-speakers more likely to treat a language as negative concord if the negative marker is an affix than if it’s an adverb? What about treating it as double negation? Here, we will use an artificial language learning experiment to test whether English-speaking participants find it easier to learn a DN language if the marker is an adverb than if it is a an affix. Participants will be taught a miniature language, including verbs, nouns, negative markers, and negative indefinites. Depending on the condition, learners will be taught a negative marker that is either an affix or an adverb, and will be taught that the meaning of sentences with two negative elements (i.e., a negative indefinite and a negative marker) is either DN or NC. Participants are then tested on how well they learn these sentences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.259 | 0.389 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".