Evidence Based Library and Information Practice Seeks Associate Editor (Evidence Summaries)
Bibliographic record
Abstract
Evidence Based Library and Information Practice (EBLIP) is seeking an Associate Editor (Evidence Summaries) to join its Editorial Team. Evidence Summaries (ES) provide critical appraisal syntheses for specific research articles. These research synopses provide readers with information regarding the original research article’s validity and reliability, thus providing information on the presence or absence of evidence with which to make informed decisions. The Evidence Summaries are a key component of this journal. ES are written by a team of experienced authors who follow a strict format to ensure consistency. All ES undergo peer review to ensure quality. Up to ten ES are published in every issue of EBLIP. The Associate Editor (Evidence Summaries) is responsible for: Monitoring a set of top research journals in librarianship for new research articles Assigning articles to ES writers Seeing ES through all stages of the publication process including assigning peer reviewers & copyeditors Working closely with other Editorial Team members to ensure a consistent, high-quality journal Maintaining a reliable, experienced ES writing team with a variety of areas of expertise Maintaining ES writing guidelines and acting as a support for the ES writing team. The ideal candidate will be well-versed in evidence based practice and critical appraisal. This position requires dedicated time on a regular basis and is labour intensive. It is therefore essential that interested persons ensure available time to devote to this position prior to applying. This is an unpaid position. Interested persons should send their resumes by October 1, 2007, to: Lindsay Glynn Editor-in-Chief lglynn@mun.ca (709) 777-6026
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 | 0.056 | 0.331 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.124 | 0.127 |
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".