Bibliographic record
Abstract
Evidence Based Library and Information Practice seeks papers on all areas of EBL/EBLIP including, but not limited to: • EBL application • Qualitative and quantitative research • Management and administrative issues related to EBP • Research tools (statistics, data collection methods, etc.) • Research education in library schools • Collaborations with other disciplines • Evidence based practice from other disciplines applicable to EBL • Harnessing evidence to support new innovations • Developing and applying evidence based tools Papers may be submitted to the following sections: • Research Articles (peer reviewed, original research) • Commentaries (non-peer reviewed, opinion-based) Submission deadlines for 2008: June 1st (for September 15th issue) September 1st (for December 15th issue) December 1st (for March 15th, 2009 issue) Further information regarding author guidelines and the online submission process can be found on the Submission section of the EBLIP website http://ejournals.library.ualberta.ca/index.php/EBLIP/index.
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 | 0.124 | 0.413 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.061 | 0.043 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.054 | 0.025 |
| Insufficient payload (model declined to judge) | 0.438 | 0.386 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".