Bibliographic record
Abstract
The Evidence Based Library and Information Practice Conference (EBLIP4) is an exciting international event that has emerged in response to the growing interest among all types of libraries in using the best available research-based evidence to improve information practice. The conference on May 6-9, 2007 in Chapel Hill, North Carolina will be followed by two days of CE. The conference provides a forum for the presentation of high quality papers and posters as well as examples of how EBLIP is being implemented in library and information settings around the globe. EBLIP4 invites submissions for contributed papers and posters including both original research and innovative applications of EBLIP in library and information management. Papers that deal with library support of evidence-based practice in other fields such as health, social work and public policy are also welcome. Additional info may be found at: www.eblip4.unc.edu Important Dates January 8, 2006 Submission deadline for abstracts for papers and posters February 11, 2007 Final decisions for accepted papers February 15, 2007 Final decisions for accepted posters March 15, 2007 Submission deadline for full papers If you have previously experience difficulties in submitting an abstract please try again using the new address provided. Should you have any difficulties, please contact Carol Perryman, EBLIP4 Co-Chair, at: cp1757@gmail.com.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.820 | 0.784 |
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".