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Record W4286640707 · doi:10.1215/00031283-9940676

Where Have All the Articles Gone? The Use of Zero Articles in Marmora and Lake, Ontario

2022· article· en· W4286640707 on OpenAlexaffabout
Lauren Bigelow, Sali A. Tagliamonte

Bibliographic record

VenueAmerican Speech · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsZero (linguistics)HistoryEnvironmental scienceGeographyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

In May 2019, the authors conducted sociolinguistic interviews with 39 White residents of Marmora and Lake, Ontario, a place founded predominately by British settlers, with a mixed farming/mining economy up until the late twentieth century. The data are rich in well-known dialect features, including preterit come, zero relative pronouns, and, most strikingly, zero articles. Praat analyses confirm that these zero articles are not simply due to phonetic reduction, and analysis of the zero variants exposes several trends. Among the oldest members of the population, the incidence of zero articles is relatively frequent, especially among men, a typical pattern for dialect obsolescence. In addition to phonetic conditioning, consistent with definite article reduction at earlier times, the data also show a strong effect of information status, consistent with patterns for the zero definite article in York, England. Older individuals use zero articles across more contexts compared to younger ones, suggesting systemic adjustments in an evolving grammatical system. The authors argue that the use of the zero articles in Marmora reflects an earlier stage in the history of both the definite and indefinite articles in English. They also consider cultural changes and psychological impacts from personal commentaries to highlight the importance of social context. This research demonstrates that rural Ontario offers key insight into the earlier stages and current state of dialects in North America.

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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.702

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 studies0.0000.001
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 head0.084
GPT teacher head0.318
Teacher spread0.234 · 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 teacher head, 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
Published2022
Admission routes2
Has abstractyes

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