Bibliographic record
Abstract
The end of yet another calendar year leads to a time of new beginnings and new challenges. For me, this is the end of my three year term as Editor-in-Chief of EBLIP, which has been a very rewarding challenge personally and professionally. I would like to thank each and every person who has contributed to the success of the journal. EBLIP relies on the talented and professional people who regularly give of their time and expertise. I particularly want to thank the Editorial Team who has worked so closely with me over the past three years: Alison Brettle, Lorie Kloda, Katrine Mallan, Jonathan Eldredge, Michelle Dunaway, and our former intern Andrea Baer. Thank you! In the past three years, EBLIP has continued to grow and thrive. We currently have more than 3400 registered readers, and an Editorial Advisory Board comprised of 70 people from 11 countries. Several of our papers have had more than 5000 pdf downloads, and the average number of downloads is 1308. I am also pleased to let you know that we have recently been accepted for inclusion in Scopus. In looking back and reflecting on the past three years, we have continued to build a strong open access journal that is relevant to LIS practitioners. We have increased the number of articles being published, and the overall number of submissions. Content is wide ranging, including multiple sectors within library and information studies. Our evidence summaries continue to be the heart of our journal, with more and more research from our field being critically appraised in this way. We have also added new sections such as Using Evidence in Practice, in which authors reflect on incorporating evidence into practice. And now it is time yet again for renewal and change. I am very pleased to welcome Alison Brettle of the University of Salford, as she begins her three year term as Editor-in-Chief. Alison has been the Associate Editor (Articles) since the very early days of publication and brings extensive knowledge of evidence based practice to the role. Alison was recently named as one of four “information hubs” by participants in a study by the U.K.’s LIS Research Coalition’s RiLIES project which is investigating the impact of LIS research on LIS practice – a great honour! EBLIP readers are lucky to have Alison take over as Editor-in-Chief!As for me, I will be sticking around as Associate Editor for a new section we are adding to EBLIP – Reviews. Review articles provide a way for librarians to obtain an overview of the evidence on a particular topic and stay current with the literature. Reviews may take on several different forms, including an overview, systematic review, meta-analysis, literature review, or state-of-the-art review, among others. For a detailed explanation of the types of reviews possible, please consult Grant and Booth (2009). We are particularly interested in reviews which contain information on the state of research on a particular topic, so if you are interested in exploring a topic and writing about it, please be in touch. This issue of EBLIP contains several written versions of presentations given at the EBLIP6 conference in June of this year. In the Feature section, we have keynote addresses, a winning presentation, and reflections on the conference from participants. As well, all of the papers in the peer-reviewed Articles section are based on presentations given at the conference. In addition to the conference content, Lorie Kloda has put together a record number of Evidence Summaries for you. Enjoy!
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.265 | 0.089 |
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".