Gramsci and the Ghost-Management of Medical Research: Revisiting Medical Journal Conflict of Interest Policies in an Age of Neoliberal Science
Bibliographic record
Abstract
Medical research, particularly with respect to pharmaceuticals, has become intertwined with marketing techniques (such as ghostwriting and the use of publication planning strategies) that systematically leverage conflicts of interest in an effort to exert greater control over the creation and dissemination of medical knowledge to support the commercial interests of industry.Drawing on a theoretical framework informed by the works of Robert Merton and Antonio Gramsci, this thesis seeks to explore how the current organizational form of medical research reflects a neoliberal conception of science characterized by its distinct normative structure.Examining these issues in the form of medical journal conflict of interest policies, this thesis seeks to evaluate the impact and efficacy of such policies in addressing the problem posed by a neoliberal conception of science. Chapter: IntroductionScience does not exist in a vacuum.Rather, it is the subject of an ongoing negotiation between a variety of social, economic and political interests each of which exerts variable levels of influence on the objectives and processes governing scientific research.The extent to which each of these forces guide scientific research can readily be seen in the specific policies responsible for governing both the creation and resulting dissemination of scientific knowledge.While the process of scientific discovery is often portrayed as a so-called ‗Republic of Science' guided by Mertonian norms emphasizing the pursuit of objective knowledge that is seen to be universally accessible and serving a broader social purpose, 1 a cursory examination reveals that the presence of external influences call into question this perceived reality.Among such influences, perhaps none is more pervasive than the entrenched belief that neoliberal ideals of competition, individualism, and market-oriented decision making function as the optimal guiding principles for economic activity, 2 and summarily can be applied to scientific research in those areas that can be readily commercialized.While these values have come to be portrayed as accurate depictions of reality, such practices are in fact nothing more than theoretical ideals.In reality, scientific research particularly within medicine has largely been shifted to a private, increasingly consolidated and controlled corporate sphere 1 Robert K.
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 | 0.069 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".