Bibliographic record
Abstract
Purpose To trace patterns of collection development expenditures between 1994 and 2004 among Association of Research Libraries' (ARL) largest and smallest public and private academic libraries, to identify the impact of serial inflation, the emergence of electronic resources and changes in the monographic market upon the buying patterns of the largest and smallest academic libraries, public and private, in the USA and Canada. Design/methodology/approach Analysis of the annual ARL statistics for collection development expenditures between 1994 and 2004, focusing upon the ten largest public, ten largest private, ten smallest public and ten smallest private academic ARL libraries. Findings Libraries have largely responded to the revolutionary changes of the last decade very conservatively, retaining their commitment to monographic acquisitions and to their paper collections even as they have built new, electronic libraries. Research limitations/implications ARL statistics present a complex picture, and libraries are not consistent in the manner in which they report their activities. The methodology does not seek a statistically precise model but seeks only to lay out a useful snapshot of library collecting patterns over the last ten years. Practical implications Academic libraries have not yet fully confronted the issues raised by changes in scholarly communication over the last decade and still have many difficult decisions ahead of the, as patterns of the last ten years may be difficult or inappropriate to sustain. Originality/value Provides a picture of collection development patterns of the largest and smallest ARL academic libaries that complements ARL's own analysis, which is based on median values.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".