The Social Construction of Dysfunction and Disorders: What Role Does the Pharmaceutical Industry Play?
Bibliographic record
Abstract
There has been widespread controversy regarding the pharmaceutical industry’s motive to promote and sell new drugs pertaining to sexual health. In this paper I will bring up a number of different authors who feel that the definition of ‘sexual health’ has been purposely redefined in order to create a market for a drug whose need is questionable. This paper investigates and raises questions about the veracity of diseases heavily promoted and marketed by the pharmaceutical industry, such as female sexual dysfunction disorder. Is it truly the female equivalent to male erectile dysfunction? Or is it the avaricious creation of pharmaceutical companies in a sly endeavor to increase sales of products like Viagra? Next, I look at the enormous impact pharmaceutical companies have had on shaping our everyday definitions of what is ‘normal’ in terms of sexual functioning. Also, the effects of standardizing diagnosis are considered, and finally, I investigate the costs of reducing sexual dysfunction to a physiological cause and how pharmaceutical industries strive to create a universalized, function-focused sexuality in which physiology dictates sexual conduct.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.128 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".