Bibliographic record
Abstract
The pressure on library spaces has been increasing over the past three decades from a variety of sources: changing institutional priorities, patron behaviours and expectations, collection development practices compounded with shrinking budgets, and evolving services and resources.Conversations around physical space combined with the library's development as a welcoming and engaging virtual space or hub contribute to "library as place" being a robust and vigorous topic of discussion.Transforming health sciences library spaces adds ten case studies to this conversation.The chapters delve into the experiences of academic, hospital, and consumer health libraries that serve populations of students, faculty, health care providers, and patients.They focus predominantly on libraries in the United States but include one case study from the Health Sciences Library at the Northern Ontario School of Medicine (NOSM) University in Northern Ontario.With the exception of some of the hospital structures and funding models, the experiences and methodologies are transferable to the Canadian context.The writing quality is clear but at times somewhat inconsistent given a variety of authors.As described in the preface, these chapters "provide insights into planning, budgeting, collecting and integrating user feedback, collaborating with leadership and architects, and thriving in the good times and the tight times".
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".