Commentary: Suggestions for guidance by academics who collaborate with digital companies – a commentary on Bourgaize et al. (2025)
Bibliographic record
Abstract
Our collective article argues for the development of a clear, shared guidance to support responsible collaborations between academic researchers and digital technology companies, particularly in the fields of education and youth mental health. Drawing on longstanding experience in edtech research, we argue that effective academia-industry collaboration requires clearer institutional support, with explicit guidance at both the contractual and community engagement levels to ensure transparency, fair reporting and the inclusion of all stakeholders. We highlight the challenges researchers face, such as limited legal support and difficulties in publishing negative results, and the need for strong contractual safeguards that protect against the suppression of negative results, define data ownership and set transparent terms for data use, publication timelines and study termination. We also advocate for formalized data-sharing protocols and a centralized, anonymized data repository governed by shared principles, enabling more rigorous cross-study analyses and supporting funders, researchers and policymakers in making evidence-based decisions.
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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 | 0.074 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.015 | 0.012 |
| Research integrity | 0.118 | 0.108 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".