<b>Auster, Ethel, and Shauna Taylor.</b> <i>Downsizing in Academic Libraries: The Canadian Experience</i>. Toronto: Univ. of Toronto Pr., 2004. 204p., acid free paper, $50 (ISBN 0802089755). LC: 2004-276481
Bibliographic record
Abstract
Downsizing in Academic Libraries reports the results of a study to document implementation strategies and determine outcomes of downsizing in Canadian libraries.The study covers fift een academic years from 1982 to 1998.The 1990s was a period of declining government support for higher education in Canada.Canadian academic libraries had to manage cutbacks while, at the same time, campus enrollments were increasing, materials costs were rising, and the purchasing power of the Canadian dollar was falling.Although the study was conducted in the twenty-six libraries that hold membership in the Canadian Association of Research Libraries (CARL), their experiences are not uniquely Canadian; U.S. libraries have managed a similar set of problems.In the 1960s, managers were hired for their abilities to manage growth.In the 1970s and 1980s, declining resources demanded that managers implement short-term strategies, such as cutt ing serials, increasing dependence on interlibrary loan (ILL), reducing hours, developing cooperative relationships with other libraries, and so on.There was still an optimism that the days of abundance would return.By the 1990s, it was accepted that libraries must change more fundamentally.This book examines those efforts.The authors briefly summarize general management literature on downsizing.They identify downsizing strategieswork force reduction, work redesign, and systemic organizational change.Each strategy is described and its impact on the individual and organization is assessed.Each then is examined in the library context.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.013 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".