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Record W4253568469 · doi:10.1017/s0008413100003698

Linguistic Anthropology in Canada: Some Personal Reflections

2005· article· en· W4253568469 on OpenAlexaffabout
Régna Darnell

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsWestern University
Fundersnot available
KeywordsLinguistic anthropologySubsistence agricultureLinguisticsContext (archaeology)SociologyAnthropologyNarrativeSpeech communityApplied anthropologyAnthropological linguisticsSocial scienceApplied linguisticsHistoryArchaeologyClinical linguistics

Abstract

fetched live from OpenAlex

Abstract Linguistic anthropology can be understood as attention to the use and communicative context of language across cultures and societies. The legacy of linguistic anthropology for both of its constituent disciplines resides in qualitative research methods and the attention paid to the particular words of particular speakers. Linguistic anthropologists have also modelled ethical ways of doing collaborative research. Canadian linguistic anthropology has been pragmatic and closely tied to the maintenance and revitalization of First Nations (Native Canadian) languages. Issues of language are inseparable from those of community and larger social processes: this can be seen in the context of traditional Algonquian languages in the Prairies as well as in the adaptation of English to First Nations purposes. The latter is a reaction to the imposition of residential schooling that alienated students from their culture, their community, and their language, and escalated language loss. Current research on life-history narratives indicates that nomadic legacies of subsistence hunting are still present in the decision-making strategies of contemporary Algonquian peoples in southern Ontario.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.243
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0650.022
Scholarly communication0.0130.004
Open science0.0030.008
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0050.000

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 head0.043
GPT teacher head0.388
Teacher spread0.344 · 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 designQualitative
Domainnot available
GenreCommentary

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
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicMultilingual Education and PolicyFrench-language works237,207