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Record W4312424984 · doi:10.7202/1093870ar

Collaborer en contexte de COVID-19

2022· article· fr· W4312424984 on OpenAlexaffvenueabout
Sylvie Hamel, Bastien Quirion, Natacha Brunelle

Bibliographic record

VenueCriminologie · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsUniversity of OttawaUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesPolitical scienceCoronavirus disease 2019 (COVID-19)SociologyPhilosophyMedicine

Abstract

fetched live from OpenAlex

Cet article se penche sur les réponses de 81 intervenants québécois oeuvrant auprès de personnes judiciarisées âgées de 16 à 35 ans à propos des défis que pose la crise sanitaire en matière de collaboration intra et interorganisationnelle. Ces intervenants, affiliés à diverses agences des milieux institutionnels et communautaires, ont rempli, entre novembre 2020 et juin 2021, un questionnaire portant notamment sur les effets de la crise pandémique sur leur capacité à travailler en collaboration. Les résultats de cette enquête montrent comment les conséquences de cette crise sont venues affecter la dimension organisationnelle de la collaboration intra et interorganisationnelle et encore davantage, sa dimension interactionnelle. En d’autres mots, ces résultats révèlent combien les relations humaines constituent un élément fondamental à la collaboration, tant celles que les intervenants développent entre eux que celles qu’ils développent avec leur clientèle. Au final, cet article porte à réfléchir sur les conditions essentielles à la collaboration ainsi que sur les attentes que l’on fait peser sur elle en faveur d’une meilleure intégration des services et d’un accompagnement mieux adapté à la complexité des trajectoires des personnes judiciarisées.

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)0.011
metaresearch head score (Gemma)0.017
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 score0.187
Threshold uncertainty score0.372

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0230.010
Scholarly communication0.0100.005
Open science0.0020.016
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.359
GPT teacher head0.443
Teacher spread0.084 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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