Translational IDC
Bibliographic record
Abstract
This workshop aims to bring together IDC authors, academic researchers, and industry practitioners to collaborate in identifying translation practices to reduce IDC's research-practice gap. To achieve this, we will (1) share successes and challenges in translational IDC research, (2) explore current IDC authors’ practices and needs to share their evidence-based research with practitioners, and (3) derive ideas with and feedback from IDC authors with hands-on activities and lightweight processes to translate their IDC research to meet the needs of industry practitioners who work on children's media and technologies. This workshop will bring together IDC community members with practitioners in industry to envision and develop pathways to a sustainable process to share IDC research.
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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 | 0.168 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.008 | 0.039 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.037 | 0.012 |
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".