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Record W7048431023

Learning biases in the interpretation of negative dependencies (testing Jespersen generalization) _ v2

2020· other· en· W7048431023 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2020
Typeother
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsnot available
Fundersnot available
KeywordsNegationInterpretation (philosophy)SentenceMeaning (existential)SerbianSemantic interpretationLogical consequenceElement (criminal law)
DOInot available

Abstract

fetched live from OpenAlex

This is a follow-up from the previous experiments pre-registred in this project. 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 give rise to a double negation interpretation, resulting in a positive meaning. (1) Niemand rent niet. N-body run NEG. “Nobody doesn’t run” → “Everybody runs” However, this is not the case in all languages. For example, despite involving two negative elements, a Serbian sentence like (2) only contains one semantic negation. In other words, the two negative elements in (2) do not each contribute a semantic negation, but rather convey a negative meaning together, yielding what is known as a negative concord interpretation. 2) Niko ne trci N-word NEG run "Nobody runs" 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. Finally, participants will be tested on their that comprehension and production of sentences two negative elements (i.e., a negative indefinite and a negative marker).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.010
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0260.003

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.

Opus teacher head0.025
GPT teacher head0.284
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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