MétaCan
Menu
Back to cohort
Record W2168635948 · doi:10.7202/011336ar

The Determinants of Participation in Non-Mandatory Training

2005· article· en· W2168635948 on OpenAlexaffvenueabout
Stéphane Renaud, Mehdi Lakhdari, Lucie Morin

Bibliographic record

VenueRelations industrielles · 2005
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversité du Québec à MontréalNatural Resources CanadaUniversité de Montréal
Fundersnot available
KeywordsPosition (finance)Multilevel modelService (business)Demographic economicsPsychologyBusinessMarketingEconomicsFinance

Abstract

fetched live from OpenAlex

This article presents an empirical research on the determinants of employees’ participation in non-mandatory training offered by their employer. The analysis model identified two groups of determinants, i.e. socio-demographic (age, gender, family responsibilities and education level) and employment-related (organizational tenure, hierarchical position and employment status). Participants, mostly female, were employees from a large Canadian service organization. Results showed that age negatively influenced participation, that women participated more than men, and that the education level was negatively related to participation. Findings also indicated a non-linear relationship between organizational tenure and participation, and that the probability of participation in non-mandatory training increased with the hierarchical position occupied. Family responsibilities and employment status were not found to be significant predictors of participation.

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)0.006
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 score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.078
GPT teacher head0.372
Teacher spread0.294 · 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

Citations35
Published2005
Admission routes3
Has abstractyes

Explore more

Same venueRelations industriellesSame topicHuman Resource Development and Performance EvaluationFrench-language works237,207