Bibliographic record
Abstract
Résumé Exercer un e-leadership est devenu un incontournable pour un nombre important d’entreprises et de dirigeants. Dans un contexte où les relations avec les employés se font principalement par l’entremise des technologies de l’information et de la communication, comment parvenir à les motiver, à les mobiliser, à créer un esprit d’équipe, à favoriser une culture forte et propice à la collaboration, à faire en sorte que tous se sentent importants et qu’ils aient le goût de s’engager ? Sachant que ces préoccupations de direction posent déjà d’importants défis dans un contexte plus traditionnel, voici que des distances géographiques et temporelles complexifient la tâche du dirigeant, qui doit parvenir à exercer un leadership autrement et par l’utilisation des technologies. L’art de gérer les distances psychologiques se présente donc comme étant une assise importante pour exercer efficacement un e-leadership . Cet article présente quatre pratiques efficaces que des dirigeants performants utilisent et les illustre par des extraits d’entrevues menées auprès de leaders à distance.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.170 | 0.052 |
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".