Bibliographic record
Abstract
As a vocal proponent of evidence based information practice, I have actively encouraged my co‐workers and anyone else who might listen, to take small steps in order to move towards incorporating research evidence into their decision making. I prompt colleagues to search in databases for research articles that may be useful, gather these articles and incorporate research results as part of their decision making process. I am often told of the difficulties faced despite one’s best attempts to make their practice more evidence‐based. A major difficulty is access to the research itself since librarians and information professionals often lack access to databases and journals in our field, particularly those of us working outside of academic libraries. An even more daunting question is how to critically read the existing research to determine whether it is valuable in a specific situation. Not all research is good, and how do we sort out the good from the bad? This inaugural issue of Evidence Based Library and Information Practice brings us one small step closer to addressing some of these concerns by equipping practitioners with information to help in their decision making process. Every issue of Evidence Based Library and Information Practice will incorporate approximately 10 evidence summaries to help readers sort through recently published research and determine if that research was done well. Evidence summaries provide a critical appraisal synthesis for a specific research article, so that practitioners may more readily determine if the evidence in that research study is valid and reliable, and whether they can apply it to their own practice. Evidence summaries are indeed a small step, but an important one. Published in an open‐access forum, these summaries will address three barriers to evidence based practice that we have faced in the past. First of all, they will bring awareness of previously published research to readers who may otherwise have missed this work. Secondly, they will bring that research to life, by engaging a dialogue with what has been published rather than allowing that published research to quietly wait to be discovered. And most importantly, the evidence summaries will allow for an objective critique of research, which in turn allows library and information professionals to make more informed decisions about the quality of the research and weigh this into their local decision making. Indirectly, reading critical appraisals informs us all of the questions we should be asking when we approach a research article and allows us to become more familiar with a critical approach to reading the literature of our field.I do not think that we could have a journal called Evidence Based Library and Information Practice without a section dedicated to critical appraisal of the existing research literature. A huge part of evidence based practice consists of filtering through the published research evidence to determine whether that evidence is valid, reliable and applicable to one’s own practice. Publishing such critical appraisal and sharing it with the whole LIS community is a central part of this journal and what we are striving toaccomplish. The Evidence Summaries Team is comprised of 10 members from Australia, Canada, New Zealand, the United Kingdom and the United States of America. They work in, and bring varying skills and knowledge from, academic, health, public and special library sectors. This is a diverse group of people who have dedicated themselves to writing one evidence summary per issue for the first year of publication. The evidence summaries follow a standardized format and undergo double‐blind peer review. A wide number of journals are scanned for potential research articles to review, and suggestions for review are most welcome. As editor of the evidence summaries, I hope that you find the format we have adopted useful, and that you will encourage colleagues to search this open‐access journal when they are looking for pre‐appraised evidence to support their decision making.
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 | 0.095 | 0.426 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.018 | 0.028 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".