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Record W4399598319

Value transmission to young Inuit children, spoken words and silence in institutional environments, Nunavik, Arctic Quebec

2017· preprint· fr· W4399598319 on OpenAlexaboutno aff
Ariane Benoît

Bibliographic record

Venuetheses.fr (ABES) · 2017
Typepreprint
Languagefr
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSilenceArcticValue (mathematics)The arcticGeographyTransmission (telecommunications)ClimatologyOceanographyTelecommunicationsComputer scienceStatisticsArtGeologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

Cette thèse a pour objet l'étude des modalités de transmission du savoir-être au jeune enfant inuit fréquentant les établissements préscolaires et médicaux. Dans le village de Kuujjuaq, le plus peuplé du Nunavik, plus de la moitié des enfants de moins de cinq ans sont inscrits dans les établissements préscolaires, tandis que tous les enfants fréquentent le service médical et l'anglais leur est familier. Les interactions verbales et non verbales sont décrites puis analysées en fonction de leur statut et de leurs effets sur le développement de l'enfant. Cette recherche qualitative s'appuie sur 150 heures d'observation dans les établissements préscolaires, près de 30 heures d'observation dans le milieu médical et sur la tenue de 30 entretiens. Les résultats de la recherche montrent que les interactions en milieu institutionnel, au sein duquel les langues parlées sont l'anglais et l'inuttitut, exercent une influence sur la transmission du savoir-être, aussi conditionnée par les circonstances de l'échange et les pratiques éducatives inuit. La thèse révèle également une conception de l'éducation fine et originale dans sa capacité à développer chez l'enfant l'autonomie et la solidarité.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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
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
Published2017
Admission routes1
Has abstractyes

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Same venuetheses.fr (ABES)Same topicIndigenous Studies and EcologyFrench-language works237,207