Bibliographic record
Abstract
EDITOR'S SUMMARY Starting her leadership year, incoming ASIS&T president Nadia Caidi noted the great success of the 2015 Annual Meeting, focusing on information science with impact. Over the next year Caidi is committed to celebrating the Association's members, connecting with other organizations focused on information issues and analyzing the Association's structure and policies to ensure its future strength. An emphasis on membership and engagement runs through each of these goals, to enhance appreciation of the Association as a collective home for those in the field of information science and technology. Members are invited to share ideas through numerous committees and task forces, listed with their chairpersons and heads, as these groups reach out to the membership over the coming year to encourage all to feel involved with ASIS&T. Looking forward to the 2016 Annual Meeting in Copenhagen, Caidi sets her sights on the Association gaining broader recognition and strength and becoming more international, diverse and inclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".