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Record W3133427800 · doi:10.1017/cnj.2020.34

‘How do you get to Tim Hortons?’ Direction-giving in Ontario dialects

2021· article· en· W3133427800 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

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2021
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVernacularCardinal directionLinguisticsVariation (astronomy)Ideal (ethics)SociolinguisticsGeographyCenter (category theory)Speech actSociologyPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

Abstract In this study, we target the speech act of direction-giving using variationist sociolinguistic methods within a corpus of vernacular speech from six Ontario communities. Not only do we find social and geographical correlates to linguistic choices in direction-giving, but we also establish the influence of the physical layout of the community/place in question. Direction-giving in the urban center of Toronto (Southern Ontario) contrasts with five Northern Ontario communities. Northerners use more relative directions, while Torontonians use more cardinal directions, landmarks, and proper street names – for example, Go east on Bloor to the Manulife Centre . We also find that specific lexical choices (e.g., Take a right vs. Make a right ) distinguish direction-givers in Northern Ontario from those in Toronto. These differences identify direction-giving as an ideal site for sociolinguistic and dialectological investigation and corroborate previous findings documenting regional variation in Canadian English.

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)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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