A Strategy for Academic Libraries in the First Quarter of the 21st Century
Bibliographic record
Abstract
The wide application of digital technologies to scholarly communications has disrupted the model of academic library service that has been in place for the past century. Given the new Internet tools and the explosive growth of digital content available on the Web, it is now not entirely clear what an academic library should be. This article is an attempt to provide a strategy for academic libraries in what is left of the first quarter of the 21st century. There are five components of the model: 1) complete the migration from print to electronic collections; 2) retire legacy print collections; 3) redevelop library space; 4) reposition library and information tools, resources, and expertise; and 5) migrate the focus of collections from purchasing materials to curating content. Each of the components of the strategy and their interactions will be considered. It is hoped that the result will provide a useful roadmap for academic libraries and the campuses they serve.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".