Agenda for Studying of Big Deal Cancellation Projects as Information Practice
Bibliographic record
Abstract
This article introduces a conceptual framework and approach for studying the information and decision-making practices of academic librarians involved in big deal cancellation projects—a type of collection malmanagement projects that are today prevalent across academic libraries in North America. We describe the nature and dynamics of big deal cancellation projects and conceptualize the quantitative and qualitative evaluations they entail. Predicated on this account, we present a theoretical and methodological agenda for empirical research. This conceptual paper goal, thus, is to describe and conceptualize big deal cancellation projects as an object of empirical research and to offer a perspective on how they can be studied as a type of information practice. Cet article présente un cadre conceptuel et une approche pour étudier les pratiques d'information et de prise de décision des bibliothécaires universitaires impliqués dans d'importants projets d'annulation d'abonnements—un type de projets de mauvaise gestion de collections aujourd'hui répandu dans les bibliothèques universitaires en Amérique du Nord. Nous décrivons la nature et la dynamique des grands projets d'annulation et conceptualisons les évaluations quantitatives et qualitatives qu'ils impliquent. En s'appuyant sur ces observations, nous présentons un agenda théorique et méthodologique pour la recherche empirique. L'objectif de cet article conceptuel est donc de décrire et de conceptualiser les grands projets d'annulation comme un objet de recherche empirique et d'offrir une perspective sur la façon dont ils peuvent être étudiés en tant que type de pratique informationnelle.
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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 | 0.045 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".