Bibliographic record
Abstract
EDITOR'S SUMMARY Nadia Caidi, 2016 ASIS&T president, reported on attending conferences of the Council of Scientific Society Presidents (CSSP), the American Library Association (ALA) and ALISE (Association for Library and Information Science Education), highlighting the groups' intersecting interests and overlapping challenges. Information issues were present across much of the CSSP conference, including a focus on human behavior and interaction, adapting to changes in scholarly publishing and maintaining data integrity and security. The Midwinter Meeting of ALA, of which ASIS&T is an affiliate, included developments in library and information services and demonstrated the strong ties between the organizations and our goals. At the ALISE meeting training and accreditation were focal points; ASIS&T will participate in ongoing discussions on the topic. Planning for the ASIS&T 2016 Annual Meeting in Copenhagen continues, certain to deliver a stimulating mix of views on the fields of library and information science and technology.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.195 | 0.123 |
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".