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Record W4388185313 · doi:10.54932/qymj5601

LES FACETTES DU TRAVAIL EN MODE HYBRIDE. Résultats d’un deuxième questionnaire distribué auprès d’employés dans le cadre d’un projet longitudinal de recherche

2023· report· fr· W4388185313 on OpenAlexfundaboutno aff
Ali Béjaoui, Sylvie St‐Onge, Ingrid Peignier, Félix Ballesteros Leivas

Bibliographic record

Venuenot available
Typereport
Languagefr
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
FundersHEC MontréalHydro-QuébecAutorité des Marchés FinanciersUniversité du Québec à MontréalUniversité de SherbrookeUniversité du Québec à Trois-RivièresConcordia UniversityPolytechnique MontréalUniversité Laval
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La pandémie de 2020 a forcé le travail à distance pour de nombreux travailleurs au Canada. Aujourd’hui, plusieurs d’entre eux continuent de travailler à distance, au moins en partie. Ce nouveau mode d’organisation « hybride » permet d’offrir aux employés une plus grande flexibilité. Quels sont les divers modes de travail hybrides adoptés par les employés ? Quels sont leurs déterminants, leurs incidences ainsi que leurs conditions de succès ? Une équipe du CIRANO mène une étude longitudinale novatrice auprès d’employé(e)s de plusieurs organisations afin d’offrir des données probantes pour aider les employeurs. Ce texte résume les principaux résultats de la première et de la seconde phase du projet menées au Québec à l’été 2022 et au printemps 2023. La troisième et dernière phase de la recherche aura lieu à l’hiver 2024.

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)0.012
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.279
GPT teacher head0.451
Teacher spread0.171 · 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 designObservational
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
Published2023
Admission routes2
Has abstractyes

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