Commentary: Industry collaboration: A call for ‘industry literacy’ – a commentary on Bourgaize et al. (2025)
Bibliographic record
Abstract
In response to the article by Bourgaize et al. (Child and Adolescent Mental Health, 2025) on academic collaborations with technology companies, we argue that we need to move beyond guidance for individual researchers; instead, there is an urgent need to develop a research infrastructure, to manage the risks of collaborating with corporations whose profits rely on the very products under investigation. Institutional transparency is essential as well as consideration of the wider ecosystem regarding conflicts of interest. Much can be learned from historical examples of 'corporate playbook' techniques, such as the gambling and tobacco industries. Specialist ethical oversight is urgently needed, which considers broader questions around commercial influence, and minimum open science standards should be mandated by research institutions, in order to preserve public trust in science. An overarching national center of expertise is needed to develop guidance, together with legislation to enforce data sharing for independent research. Lastly, we suggest detailed questions should be asked about who may have the most to lose and the most to gain from a collaboration; academics should equip themselves not just with digital literacy, but also with 'industry literacy' to navigate this complex relationship.
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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 | 0.024 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.105 | 0.118 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".