Gérer les sous-traitants dans un contexte de pénurie de main-d’œuvre : le rôle complexe des managers de proximité
Bibliographic record
Abstract
Cet article propose une réflexion sur les enjeux managériaux liés à la gestion quotidienne de la sous-traitance. À partir d’une enquête menée auprès de conducteurs de chantier relevant de grandes entreprises belges de construction, nous montrons comment la gestion quotidienne des sous-traitants conduit à de nombreuses situations d’arbitrage visant à permettre la bonne exécution des projets. Nous détaillons l’évolution des pratiques de gestion quotidiennes, qui visent de plus en plus à attirer et à fidéliser les sous-traitants devenus particulièrement rares et indispensables, dans un contexte de pénurie structurelle de main-d’oeuvre locale spécialisée. Cette analyse nous conduit à une discussion mettant en évidence 1) l’importance du management de proximité dans la gestion et l’entretien des relations de sous-traitance, 2) les interdépendances générées par l’état actuel du marché du travail et 3) les risques de ces évolutions pour les professions de manager de proximité.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".