Bibliographic record
Abstract
EDITOR'S SUMMARY Change and renewal characterize ASIS&T with new initiatives, programs and management developments to better serve the membership. The first ASIS&T Regional Meeting, held April 15, 2016, at Rutgers University offered stimulating panels and showed student leaders' dedication to building the Association. Members' suggestions have led to featuring short video presentations by doctoral students on the ASIS&T website and explorations into professional concerns and accreditation reforms. The ASIS&T Information Architecture and Research Data Access and Preservation Summits thrive, rallying dynamic and important communities within the profession. ASIS&T management changes include the plan to hire a communications officer to boost the Association's visibility and responsiveness to members and the pending retirement of executive director Dick Hill after nearly 30 years of service. Preparations for the 2016 Annual Meeting, to be held in Copenhagen, Denmark, are in full swing, including opportunities to bid Dick thanks and farewell.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.285 | 0.195 |
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".