Bibliographic record
Abstract
Résumé Une approche individualiste du leadership n’est plus adéquate étant donné la complexité croissante de l’environnement de travail. Bien que les théories du «grand leader» persistent, la plupart des spécialistes du leadership estiment que les organisations qui ont du succès se distinguent par un leadership réparti, collectif et complémentaire. Dans l’exploration de cette forme de leadership, cet article dégage huit archétypes du leadership, qui sont autant de modes de direction. Les caractéristiques et les limites de chaque archétype sont examinées. De plus, on y apprend comment un dirigeant peut travailler en équipe avec les personnes présentant ces huit styles de leadership ou encore comment il peut superviser ces personnes. Ces descriptions ont mené à l’élaboration d’un Questionnaire des archétypes du leadership, qui permet aux leaders de découvrir leur archétype dominant. La recherche révèle aussi que les individus possèdent le plus souvent des caractéristiques de plusieurs archétypes. Par ailleurs, chaque archétype s’avérera plus ou moins efficace selon la situation que connaît l’organisation. C’est pourquoi l’équipe de dirigeants idéale devrait se composer de personnes qui possèdent divers archétypes dominants du leadership.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".