Bibliographic record
Abstract
Leemon McHenry (2009) raises a set of issues about the normative stances I take in my recent paper on publication planning in the pharmaceutical industry (Sismondo, 2009). In objecting to my stances, McHenry also sug gests that the problems faced in medical research show the constructivist tradition in Science and Technology Studies (STS) to be crucially flawed. His position on my normative stances is an intuitively attractive one, but I think that it misses important features of clinical research and publication, and so misses opportunities. First, though, while it might merit more dis cussion, let me set aside his position on constructivism. McHenry's quick argument against constructivism is that it cannot allow one to distinguish between 'genuine and sham' science, or between ordinary choice-laden science and misconduct. However, we can easily make a concrete version of these distinctions by relying on community norms, standards, or ideals: in general, behavior that constitutes scientific misconduct fails to live up to scientific norms. We can even go further, and argue that some community norms, standards, or ideals don't live up to others, or don't live up to extra-scientific ideals. In so doing, constructivists can critically engage with academic medicine if they choose to. I therefore applaud the work of people such as McHenry and his col league Jon Jureidini, and the many other vigilant critics of industry-sponsored trials (for a few citations, see McHenry's [2009] comment). I happily join them when the opportunity arises. The problem is not that criticism of the flaws of individual trials or publications is too bold a move, but that restrict ing criticism to flawed trials and publications is too cautious. The thorough ghost management of the pharmaceutical literature suggests that we should want to go much further. If I did not make that clear enough in my paper, I thank McHenry for pushing me to amplify the point. McHenry (p. 000) says, 'If the results of industry-sponsored clinical tri als were reported honestly, then aside from the question of deception and plagiarism, ghostwriting would not present a serious concern for advancing knowledge.' I take issue with that assumption.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".