Bibliographic record
Abstract
I have just returned from the ASIS&T Annual Meeting in Vancouver. It was well attended and had a number of very interesting sessions—user information behavior and data curation seemed to be particularly hot topics. The poster sessions were also extensive and very popular—the coffee helped. And there was free wi-fi in the reception area! We look forward to possible additional joint meetings with our excellent hosts, the Canadian Association for Information Science. The Society also announced at the meeting that next year's Annual Meeting in Pittsburgh will be held in conjunction with the DC 2010, the Dublin Core Metadata Initiative Conference, and a summit Research Data Access and Preservation will be held in conjunction with the 2010 IA Summit in Phoenix in April. Also, Information Today has purchased the annual Search Engine Conference, and ASIS&T members will be able to get better prices for that event. The Board of Directors also announced that JASIST will begin to carry six literature review articles each year to replace the Annual Review of Information Science and Technology. Many authors consider it a more visible venue, and the journal will benefit from the increased usage that such articles generate. Because of the timing of this issue, much reporting of the Annual Meeting will be postponed to our February/March issue, but we are able to begin our coverage of the meeting with a few special features in this issue. These items include President's Pages from both the outgoing and incoming ASIS&T presidents; the speech given at the Awards Luncheon by Carol Tenopir, the recipient of this year's Award of Merit; and an update on the work of the information professionals task force. Also, our special section in this issue is based on an Annual Meeting panel covering the role of facts and events in information retrieval. Finally, Thom Haller, our new associate editor for information architecture, discusses what motivates him to pursue this profession.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.533 | 0.421 |
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".