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Record W2977372813 · doi:10.7202/1064663ar

VERS UNE RECONNAISSANCE DE LA PLURALITÉ DES SAVOIRS EN TRAVAIL SOCIAL

2019· article· fr· W2977372813 on OpenAlexvenueno aff
Lisa Ellington

Bibliographic record

VenueCanadian social work review · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyPolitical scienceArt

Abstract

fetched live from OpenAlex

Le travail social est une profession qui s’est grandement transformée au cours des dernières décennies, modulée par les diverses transformations sociales, politiques et organisationnelles de la société dans laquelle elle évolue. Or, on retrouve aujourd’hui des recherches de plus en plus diversifiées, où plusieurs visions du travail social coexistent. Ces visions multiples s’accompagnent de valeurs et de principes, mais également d’idéologies tantôt dominantes, tantôt marginales. Les peuples autochtones sont l’un des groupes les plus marginalisés et leurs visions du monde le sont tout autant au sein de la profession. Aujourd’hui, il semble y avoir une volonté de reconnaître la pluralité des savoirs en travail social. Le présent article poursuit cet objectif en présentant le paradigme autochtone en recherche. Il s’agit d’une réflexion théorique qui s’articule autour du contexte historique menant à la création du paradigme, une description de ce qui le compose de même qu’une présentation de quelques exemples de son utilisation par des chercheurs en travail social. Enfin, l’article met en lumière certains enjeux persistants quant à la reconnaissance du paradigme autochtone au sein de la profession.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.004
Science and technology studies0.0030.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.033
GPT teacher head0.310
Teacher spread0.277 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations7
Published2019
Admission routes1
Has abstractyes

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