Bibliographic record
Abstract
I'm pleased to announce that registration for the 2008 CHLA/ABSC Conference to be held in Halifax, NS, from May 26 to 30, is now open. Visit the conference website at http://www.chla-absc.ca/2008/ and click on "Registration". The rate for the full conference for members is $300 for early bird registration. After April 11, a late charge of $80 will be charged. CE details for most sessions are now on the website. Please note that registration for each of these sessions is limited to 25. A wide array of topics is being presented. Project Management Joanne Fraser Learning Styles: Are You Smarter Than a Millennial? The challenges of generations working and learning together Daniel Phelan and Sarah Wickett Getting Started in Research Andrew Booth Evidence-Based Practice Ann McKibbon Grey Matters! Finding Grey Literature Sarah Normandin and Amanda Hodgson Canadian Copyright Law: Current Issues for Librarians Teresa Scassa Current Awareness Tools – Web 2.0 Ryan Deschamps and Kelli Wooshue Creating Online Tutorials Gwendolyn MacNairn Come for the programme, come for the social events, come for the lobster, come for the history, come for the famous down east hospitality! A lot of effort is being expended to ensure this year's conference will be a very successful one. Tim Ruggles Publicity Committee CHLA/ABSC 2008
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.583 | 0.454 |
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".