Commentary: Reconsidering Pharmaceutical Research and Development Investments
Bibliographic record
Abstract
Following Lee and colleagues' (2023) article explaining how Canadians are being shortchanged by drug companies when it comes to investments in research and development (R&D), this rejoinder adds context and appends two other very problematic elements in the debate between wishful narratives over the industry's contribution in R&D and actual numbers. First, even the current stricter definition of R&D investment might simply be too large considering that elements such as seeding trials - a well-known marketing device - can be accounted for as R&D expenditures. Second, this rejoinder identifies how Statistics Canada acted in concert with Innovative Medicines Canada to reinforce the industry's preferred narratives around R&D expenditures. This situation puts into question the trustworthiness of Canada's statistical agency.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.137 | 0.084 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".