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Record W7099684551

Sounding Canadian from Coast to Coast: Regional accents in Canadian English

2008· article· en· W7099684551 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsColonialismIdentity (music)Cultural identityVocabularyUkrainianImmigrationAssertionEthnic group
DOInot available

Abstract

fetched live from OpenAlex

language variant that has cast off the yoke of both external and internal colonialism. In its pages, Canadians found an affirmation that they are different from Americans, as the elusive Canadian identity took tangible form in all the words they had been using all their lives without knowing they were Canadianisms. But the vocabulary recorded in the dictionary also reflected an assertion of non-anglophone cultural identities within Canada. The numbers of words borrowed into English from immigrant languages such as Ukrainian and Italian demonstrated a recognition of those cultures by Canadian society which would have been unimaginable two generations ago. The increasing influence of Canadian French, especially in Quebec, coincided with the growing rejection of perceived Anglo colonialism in that province, while the Native peoples ’ cultural renaissance and rejection of definition by the colonializing culture has resulted in a wave of new self-designations and words designating Native cultural realities. But some remnants of colonialism linger, as demonstrated by the reluctance of some users, often virulently expressed, to accept some of the facts about Canadian English recorded in the dictionary.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.004
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.028
GPT teacher head0.216
Teacher spread0.188 · 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
Published2008
Admission routes1
Has abstractyes

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