Atlantic Schools of Business: proceedings 2011 annual conference (41st): Charlottetown, Prince Edward Island, University of Prince Edward Island, September 30 - October 2
Bibliographic record
Abstract
There is a great deal of work to coordinate and administer all of the activities of the Atlantic Schools of Business Conference.In particular, the coordination of activities from the call for papers to the acceptance process is very extensive.We would like to thank the many people involved to help us with this central aspect of the conference.Thanks to Ms. Amanda Strongman who took on the initiative and perseverance to automate the paper submission process using Easy Chair this year.The use of this tool provided many insights from an organizing perspective that were invaluable.We would also like to thank the track chairs (see next section for a track chair list) who provided timely and helpful feedback about how to improve the process and managed the review process for the papers submitted to their tracks.Their commitment to getting reviews for all papers and providing feedback to authors involved an immense amount of work which we are thankful they took on.Finally, we would like to thank all the reviewers who took the time to review papers in their area of expertise and offer constructive feedback to authors to help improve their work.This process relies on volunteers from across the region.We are grateful that the spirit of academic development and achievement is alive and well!Your contributions in this area make the ASB conference possible!Sincerely,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.275 | 0.059 |
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".