Bibliographic record
Abstract
Four years ago, Michelle Holmes, Wendy Chen and collegues reported a significant negative correlation between aspirin use and breast cancer (Holmes et al. 2010).This summer, they noted that no randomized trials have been initiated that test this potentially important association.Why not?Pharmaceutical companies fund most drug research; there is no profit in aspirin.This explanation is incomplete.The deeper issue is a mismatch between the public interest in advancing research, and the interests of the institutions that governments subsidize in different ways for that purpose.In addition to patent protection, governments directly fund public granting agencies and provide the tax relief offered by private charities.Like pharmaceutical companies, these have their own "stakeholders" and objectives.Nobody, it appears, is interested in aspirin. RésuméIl y a quatre ans, Michelle Holmes, Wendy Chen ses collègues faisaient état d'une importante corrélation négative entre l'utilisation de l' aspirine et le cancer du sein (Holmes et al. 2010).Cet été, elles notaient qu' aucun essai aléatoire n' avait encore été amorcé pour tester ce lien important.Pourquoi?Ce sont les sociétés pharmaceutiques qui financent la plupart des recherches sur les médicaments; or, il n'y a aucun profit à tirer avec l' aspirine.Cette explication est incomplète.L' enjeu central est un décalage entre l'intérêt public pour la recherche avancée et les intérêts des institutions que les gouvernements subventionnent à cette fin, de diverses façons.En plus de la protection des brevets, les gouvernements financent directement des organismes subventionnaires publics et offrent un allègement fiscal grâce au statut d' organisme de bienfaisance.Tout comme les sociétés pharmaceutiques, ces organismes ont leurs propres « parties prenantes » et leurs propres objectifs.Il semble bien que personne ne s'intéresse à l' aspirine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".