Understanding Health and Medicine: A Critical Examination of Governance, Surveillance and Control within Contemporary Culture
Bibliographic record
Abstract
Within contemporary western society, health and medicine understandings are often taken for granted, left unquestioned and undisturbed. However, the author of this paper looks to uproot and critically examine much of what medical professionals, scientists, and patients alike have come to understand as ‘normal’. Thus, an assessment of the ways in which the neo-liberal model, the creation of the abnormal/normal binary and social discourses combine in order to enact control, surveillance and governance, will be considered. Then, through the use of Foucauldian theory, a discussion of the implications of such ubiquitous and omnipresent social processes such as surveillance, control and governance will be considered. Furthermore, the neo-liberal model will be presented in greater detail to illustrate the ways in which privilege is cast unto those who embody that of the archetypal citizen. Additionally, social theorists Giddens and Beck will be considered as they offer critical key concepts – such as that of the risk society – which will help to better contextualize the larger theoretical frameworks that exist and pertain to health and medicine. In conclusion, Foucault’s concept of the panopticon will exemplify the ways in which surveillance, control and governance are irrevocably intertwined at a variety of levels to ultimately create citizens whom conform to government beliefs and ideals.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.017 | 0.174 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".