From the chair
Bibliographic record
Abstract
Happy New Year! I hope this year brings you health, happiness, and good cheer!Thanks to those of you who attended the "Sixth Annual ACM-SIGMIS Reception," held as an ACM-SIGMIS event prior to the International Conference on Information Systems (ICIS2006) in Milwaukee, WI USA. It was great fun seeing everyone. Please visit ACM-SIGMIS's website at: http://www.acm.org/sigmis to view photos from this event. Mark your calendar now for next year's reception, scheduled for Saturday, December 8, 2007, from 5:30-7:00pm, prior to ICIS2007 to be held in Montreal, Canada, December 9-12, 2007.Just a reminder for this year's CPR2007 conference and Doctoral Consortium: Conference: ACM-SIGMIS Computer Personnel Research 2007 Conference Theme: "The Global Information Technology Workforce" When & Where: April 19-21, 2007, St. Louis, Missouri, USA Doctoral Consortium: April 19, 2007 Website: http://www.sigmis.org .The beautiful Blue Ridge Mountains of North Carolina, USA is the venue for next year's CPR2008. Please consider sending a submission by the October 2007 deadline. Your participation is welcome!Also, please consider attending the upcoming conferences this year with which we are in-cooperation: Autonomous Infrastructure, Management, and Security (AIMS), http://www.aims2007.org; Business Information Systems (BIS), http://bis.kie.ae.poznan.pl; International Conference on e-Business (ICE-B), http://www.ice-b.org; International Conference on e-Business and Telecommunications (ICETE), http://www.icete.org; and International Conference on Enterprise Information Systems (ICEIS), http://www.iceis.org.As always, thanks for your support of SIGMIS. We encourage your involvement. Please contact any one of us with suggestions or other input.We look forward to seeing you at ACM-SIGMIS CPR'07!
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.617 | 0.559 |
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".