Future of academic and health libraries: personal perspectives
Bibliographic record
Abstract
I am frequently asked about the future of academic libraries-by my staff, my colleagues and students at Carleton-I think there is a view that libraries are becoming less visible in the Internet age.It is true that many of our users do not visit the library building but they certainly use our digital resources.Others are still comingparticularly the undergraduates-but they tend to use the library as a study hall/community centre.That is the greatest challenge for me-shaping the library to accommodate a growing diversity of needs-from undergraduates who want space and freedom to study in flexible groups with access to computers and furniture they can move around; to graduate students who want quiet, individual, reflective spaces with access to extensive collections; to faculty who want everything delivered electronically to their offices.With so much of our digital collection bought through national consortia, there is not much to differentiate us from other academic librariesapart from our special collections and services.I am encouraging further diversification into these areas, for example, we are actively seeking faculty papers and research data and are acting as a digital and paper repository for the research collections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.039 | 0.029 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".