Titre/Suivre les traces d’une filature : exposer ses enjeux méthodologiques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it