L’autopraxéographie, une méthode pour participer à la compréhension de la complexité de l’entrepreneuriat
Bibliographic record
Abstract
L’entrepreneuriat est un domaine complexe. L’autopraxéographie est une méthode à la première personne qui peut permettre d’explorer des éléments de cette complexité. Cet article vise à expliquer les spécificités de cette méthode et à en montrer des exemples d’utilisation pour participer à la compréhension de l’entrepreneuriat. Pour ce faire, il commence par présenter la complexité pour aborder l’entrepreneuriat, par la suite il explique l’autopraxéographie. Ce faisant, il expose un panorama des méthodes à la première personne, le paradigme épistémologique constructiviste pragmatique (PECP), les spécificités, le processus et les limites inhérentes à ce type de méthodes. Enfin, cet article propose de se référer à plusieurs exemples d’articles ou communications ayant utilisé cette méthode en vue d’explorer certains éléments liés à la complexité entrepreneuriale.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".