Bibliographic record
Abstract
Cette étude cherche à mieux comprendre comment la collaboration fonctionne dans les équipes virtuelles (EV) en se penchant sur les cadres interprétatifs à travers lesquels les membres font sens de leur expérience. À l’aide des concepts de sensemaking, de sensegiving et de métaconversation, nous avons réalisé une analyse narrative de récits réflexifs et de conversations d’étudiants ayant participé à une collaboration en EV. Nous avons cherché à observer comment le sensemaking individuel et les interactions participaient à y définir le sensemaking collectif. Nous avons observé une absence de sensemaking collectif au niveau de l’équipe qui s’expliquerait par l’incapacité de ses membres à négocier un collectif qui les représenterait tous. Elle illustre comment des cadres interprétatifs en compétition entravent la construction et le partage de sens; l’importance du sensegiving dans le développement d’une EV; et comment la perception des contraintes technologiques de la collaboration dépend des cadres interprétatifs qui servent à les appréhender.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".