Bibliographic record
Abstract
EDITOR'S SUMMARY Building connections between ASIS&T and other information‐oriented organizations is progressing through collaborations with the Society for the Social Studies of Science (4S), the Council of Scientific Society Presidents (CSSP) and the Association for Library and Information Science Education (ALISE). With data management a critical issue for scientific societies, the CSSP will focus its spring meeting in May 2016 on theoretical and practical issues around data, having ASIS&T and 4S jointly present the opening session. ASIS&T president Nadia Caidi serves on the CSSP executive board and co‐chairs its scholarly publications and data committee. ASIS&T ties with ALISE are growing stronger through their joint hosting of a Presidential Session on Accreditation at the ASIS&T Annual Meeting in Copenhagen in October 2016. That meeting will also feature sessions on diversity and inclusion and opportunities to meet journal editors and to hear keynote speakers exploring bridges between industry and research. Other upcoming meetings include the ASIS&T Regional (East Coast) Meeting and ASIS&T Taipei Chapter workshop, both in April 2016.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.255 | 0.169 |
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".