Titre/Suivre les traces d’une filature : exposer ses enjeux méthodologiques
Bibliographic record
Abstract
La filature ( shadowing ) est une technique de collecte de donnees qui est encore peu mobilisee dans les contextes organisationnels. De fait, il y a peu d’etudes qui rendent concretement compte des implications propres a la filature video. C’est pourquoi je propose d’en suivre les traces a partir d’un cas precis : la filature video d’un nouvel officier des Forces armees canadiennes (FAC). Mon experience sur le terrain permettra d’exposer les enjeux methodologiques de cette technique. Je presente les particularites de la filature video eu egard au recrutement, a la captation video, au temps passe sur le terrain et aux relations avec les acteurs de l’organisation. J’invite aussi a poursuivre la discussion en abordant les enjeux pratiques de la filature. Shadowing is a data collection technique not frequently used in organizational contexts. In fact, there are only few studies concretely reflecting the implications of video shadowing. That is why I propose to follow the tracks of one specific case: the shadowing of a new Canadian Armed Forces’ officer (CAF). My field experience will expose the methodological challenges of this data collection technique. Indeed, I present the particulars of video shadowing in regard to recruitment, video recording, time spent in the field and relationship with organizational actor. I also calls for further discussion on the practical challenges of shadowing.
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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 | 0.079 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".