Bibliographic record
Abstract
The Evidence-Based Librarianship Interest Group (EBLIG) is pleased to offer a pre-conference session at CLA in Vancouver. The conference website http://www.cla.ca/conference/2008/ has been updated with program information. Reading Between the Lines: How to Study a Paper Whether you are using an evidence-based model of practice, participating in a journal club, acting as a peer reviewer, or are a regular reader of research articles, learning how to critically evaluate a paper is an essential skill. Critical evaluation requires the reader to ask specific questions regarding the research methodology, data analysis and the presentation of results. These questions will be identified and discussed in this workshop, as several evaluation tools will be presented. Participants will work in groups in which they will critically evaluate a research article and present their findings. This lively, hands-on workshop will provide participants with the necessary tools to approach and challenge research with inquisitiveness. Speaker: Lindsay Glynn Acting Head, Public Services Health Sciences Library Memorial University of Newfoundland Registration fees: CLA EBLIG Members: $90.00 CLA Members: $100.00 Non-Members: $120.00 1:00 pm - 5:00 pm - Wed., May 21st, 2008 Session is limited to 35 delegates. Registration includes: One coffee break. This session is organized by: Evidence-Based Librarianship Interest Group (EBLIG) of CLA
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.339 | 0.223 |
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".