Bibliographic record
Abstract
TH E term "academic library" often calls to mind the image of a building-but a building is merely an empty space.So what makes a library what it is?We-library workers-make libraries.Human beings and their labour are behind everything a library is and everything that it does.Yet we suffer from a tendency to dissociate "the library" as a construct from the library work which animates it and gives it life.In light of the significant professional and societal challenges facing library work-and the new opportunities which spring from them-a renewed emphasis on the individual and collective dimensions of library labour is gathering pace.This reorientation is as timely as it is necessary, reflecting the rapidly changingand often difficult-sociocultural and socioeconomic contexts which give shape to our work.Budget cuts, service constraints, and ongoing library reorganizations have become a fixture of many library workers' professional lives.Our work is further conditioned by de-skilling, deprofessionalization, and corporatization-but also by
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.104 | 0.076 |
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".