Proceedings of the 3rd International Workshop on Extreme Heterogeneity Solutions
Bibliographic record
Abstract
This volume contains papers presented at the 2024 3rd International Workshop on Extreme Heterogeneity Solutions (ExHET 2024), an in-person/virtual event held on March 3rd, 2024�This year, we received three paper submissions, three of which were accepted as oral presentations� Each contributed paper was rigorously peer-reviewed by reviewers from a large pool of technical committee members and other international reviewers in related fields�The success of ExHET 2024 depends on the contributions of many individuals and organizations� With that in mind, we thank all authors who submitted their work to the conference� The quality of submissions this year remains high, and we are satisfied with the quality of the results procedure� The organizing committee also thanked the members of the Technical Planning Committee and the chairman of the meeting for their strong support� The organizing committee is responsible for reviewers who voluntarily sacrifice valuable time to evaluate the manuscript and provide authors with useful feedback� Finally, a workshop only succeeds with the strong support of its participants� We would like to thank all the authors and attendees for participating in the workshop and hope you have a stimulating and fruitful time� vii
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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".