The Embedded Records Manager: A Pilot Study Emphasizing the Importance of Community as a Key to Success
Bibliographic record
Abstract
The paper begins with a brief institutional context, including a summary of the main information management challenges facing the Faculty of Graduate and Postdoctoral Studies team. This is followed by a brief analysis of some potential solutions that were explored and the rationale for eventually selecting an embedded information professional. Next, we review some relevant literature and identify seven key criteria for successfully embedding an information professional, with a particular emphasis on those criteria concerned with community-building. Cet article commence par une brève mise en contexte institutionnelle, y compris un résumé des principaux défis de gestion de l'information auxquels l’équipe de la Faculté des études supérieures et postdoctorales fait face. Elle est suivie par une brève analyse de quelques solutions possibles qui ont été explorées et la raison qui pourrait conduire éventuellement à la sélection d'un professionnel de l'information intégré. Ensuite, nous passons en revue certains documents pertinents et identifions sept critères clés pour intégrer avec succès un professionnel de l’information, avec un accent particulier sur les critères concernés par la construction de communauté.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".