Bibliographic record
Abstract
Evidence based librarianship: we hear about it, we read about it, some of us practice it, some of us aren’t quite sure what it’s allabout. Information about EBL is relatively prolific of late and can be found in numerous journals, websites and books. It is indexed in databases and article indices across the disciplinary board – MEDLINE, CINAHL, ERIC, PsycInfo, DOIS, LISTA, etc. Evidence Based Library and Information Practice is the first journal whose primary focus is on EBL: what it is, how it affects the decisions we make, how we look at and perform research, and how it allows us to make more informed decisions based on the best available evidence. EBLIP, not unlike the practice of EBL, is international in scope and unlimited in its application and utility. Getting EBLIP off the ground has been a fascinating and rewarding experience, and I have several key people to thank for making the process seamless and satisfying. EBLIP is the brainchild of Su Cleyle, of MemorialUniversity of Newfoundland and Denise Koufogiannakis, of the University of Alberta. Su and Denise got the ball rolling early in 2005 while researching EBL on sabbatical and quickly pulled together an enthusiastic team of editors. As original Editor‐in‐Chief, Su’s vision, drive and unbeatable organization resulted in EBLIP becoming a reality more quickly and completely than we could have imagined. As Associate Editor (Evidence Summaries), Denise has begun to compile and disseminate a significant and highly useful collection of critically appraised synopses of research articles. The Evidence Summaries section allows EBLIP to reach well beyond publishing research and adds a component that our field has, until now, been lacking. In addition to her tireless work in this area, I thank Denise for keeping the momentum going throughout the entire start‐up process. EBLIP is published by University of Alberta Learning Services using OJS (Open Journal Systems). We are very fortunate to have Pam Ryan, from the University of Alberta, as our Production Editor. In addition to the mammoth task of creating an open access, peer reviewed electronic journal, we also were faced with the tasks of familiarizing ourselves with new software and creating a visual appearance. Pam’s knowledge, ability and creativity, not to mention her endless patience with the rest of the editorial team, have resulted in the professional presence of EBLIP. Her work is not transparent to the readers, but paramount to the editorial team and the journal. Finally, I would like to draw attention to two teams that are essential to the success of EBLIP: the Evidence Summaries Team and the Editorial Advisors (peer reviewers). The diversity, specialization and professionalism of the teams’ membership allow us to explore all avenues of EBL and its practice and utility in all environments. Members from our teams range from journal editors to university professors to school and systems librarians. Their contributions, not only to EBLIP but also to the body of EBL knowledge, are substantial. As readers, I hope that you will find the information in EBLIP useful, interesting, provocative, and applicable to your work. I hope that you will be able to use the information we present to you to aid in your decision making regarding policies, collections, procedures, marketing, etc., and I particularly look forward to hearing how you may have used this information in your practice (whether it worked or not!).
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.348 | 0.215 |
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".