Bibliographic record
Abstract
We have previously reported (HILJ, March 2002) on the First Ever Evidence-based Librarianship (EBL) Conference, hosted by the School of Health and Related Research (ScHARR) at the University of Sheffield on 3rd and 4th September 2001. 1 The Conference featured two particularly noteworthy international contributions.The first was by Jonathan Eldredge, former Chair of the Medical Library Association's Research Section.Eldredge reported how the concept of EBL has developed over recent years in the United States before attempting to extrapolate its further uptake and evolution over the next two decades.The second was by Ellen Crumley, now Editor of the premier Canadian medical library journal Bibliotheca Medica Canadiana.Crumley described how she and a number of colleagues have attempted to create a forum for EBL at a local level.Both of these papers are featured in this issue of HILJ.It is therefore fitting to review developments in the UK against such an international backdrop.With upward of 130 delegates in attendance at the Conference, it is clear that EBL is a concept whose time has come.Whether this initial enthusiasm translates into sustained action in changing the culture of health librarianship remains to be seen.However, it is interesting to learn that the idea of an EBL journal club has proved sufficiently stimulating to lead to at least one such initiative in Trent Region set up by enterprising librarians in Nottingham.Similarly, it was suggested at the Conference that, in advance of the appearance of systematic reviews, librarians should seize oppor-
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 | 0.116 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 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".