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Record W4400478342 · doi:10.1017/9781108983624.005

Cross-Variety Comparisons

2024· book-chapter· en· W4400478342 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVariety (cybernetics)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter reports on trends of continuity and divergence within the heritage generations examined and between heritage and homeland varieties. It discusses the degrees of similarities between the varieties in terms of (a) rates of use of innovative forms and (b) conditioning factors in the constraint hierarchy. The three variables examined are voice onset time (VOT, n=8,909), case-marking on nouns and pronouns (CASE, n=9,661), and variable presence of subject pronouns (PRODROP, n=9,190), each in three or more languages. The similarity in rates and conditioning effects across generations for (PRODROP), examined in seven languages, particularly contrasts with findings for this variable in experimental paradigms. Similarly, findings of little simplification or overgeneralization of the case system in three languages stands in contrast to the outcomes of several previous studies. (VOT) shows a drift toward (but not arriving at) English-like values for only some of the languages examined. For each variable, models are presented and interpreted; a table then details which aspects of the analysis contribute to the interpretation of stability and of each type of variation.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.975
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.261
Teacher spread0.199 · 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