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Record W2967804359 · doi:10.1177/0261927x19868974

Assessing 40 Years of Group Vitality Research and Future Directions

2019· article· en· W2967804359 on OpenAlexafffundabout
Richard Y. Bourhis, Itesh Sachdev, Martin Ehala, Howard Giles

Bibliographic record

VenueJournal of Language and Social Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité du Québec à Montréal
FundersRoyal Society of CanadaUniversité du Québec à MontréalUniversité de LorraineMcGill University
KeywordsVitalityPsychologyKey (lock)SociologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

This article provides key group vitality concepts followed by a selective overview of four decades of research on vitality issues. Group vitality is what makes language communities behave as distinctive and active collective entities within multilingual settings. Three structural factors combine to foster strong to weak group vitality: demographic factors, institutional support, and status. The objective vitality framework uses available census and sociolinguistic indicators to measure the relative vitality of minority and majority language communities in contact. Two case studies show the crucial role of language policies in improving or undermining the vitality of language minorities in Canada. Studies of subjective perceptions of group vitality are reviewed as they relate to language and communicative outcomes. Key vitality models are noted along with future research directions highlighting the need for a theoretical integration of the vitality framework.

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)0.075
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.925
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.073
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0130.017
Science and technology studies0.0040.009
Scholarly communication0.0110.026
Open science0.0040.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.141
GPT teacher head0.592
Teacher spread0.450 · 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.

Study designNot applicable
DomainEvaluation
GenreReview

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

Citations42
Published2019
Admission routes3
Has abstractyes

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