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Record W2115068566 · doi:10.1017/s0954394505050015

The ain't constraint: Not-contraction in early African American English

2005· article· en· W2115068566 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLanguage Variation and Change · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsYork University
Fundersnot available
KeywordsContraction (grammar)American EnglishNegationAin'tLinguisticsHistoryHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Studies of negation in African American English (AAE) typically focus on its most salient exponents, ain't and negative concord. Because ain't arose during the development of auxiliary- and not-contraction in Early Modern English, an interesting question is whether constraints on ain't can be attributed to more general constraints on contraction. This article examines the constraints on not-contraction in three varieties argued to be representative of Early AAE. Although the analysis is complicated by the ever-narrowing variable context of ain't and by the competition of not-contraction with auxiliary contraction, results are largely parallel across the three varieties, pointing to a common origin. The parallels between ain't and not-contraction provide evidence that ain't is the extension of more general processes of contraction. The most consistent effect, the presence of negative concord, is argued to reflect a recurrent process of reinforcement in the history of English negation.The data on which this study is based were extracted from corpora housed in the Sociolinguistics Laboratory at the University of Ottawa. I gratefully acknowledge Professor Shana Poplack's permission to use these data. Earlier versions of this article were presented at the annual meeting of the American Dialect Society (Washington, DC, January 2001) and the third U.K. Language Variation and Change conference (York, U.K., July 2001). The analysis benefited from discussions with and comments from Greg Guy, Dennis Preston, Jennifer Smith, and Gerard Van Herk, as well as several anonymous reviewers. Special thanks go to Sali Tagliamonte and Malcah Yaeger-Dror, whose comments substantially improved the article, and to Anthony Warner for help with translating the Old English examples. Any remaining errors are my own responsibility.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.019
GPT teacher head0.290
Teacher spread0.271 · 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