Confessions of the flesh and biopedagogies: discursive constructions of obesity on <i>Nip/Tuck</i>
Bibliographic record
Abstract
Informed by the work of Michel Foucault, the authors discuss the Nip/Tuck episode entitled "Momma Boone" and how it discursively constructs "obesity". They show how this popular media text can be understood as a crystallisation of the dominant discourse surrounding fat bodies. In the process, how the episode can be seen as a "biopedagogy" that instructs its viewers in how to think and feel about the fat body is examined. Foucault's formulation of the confessional is seen to be useful to theorise the ways in which biopedagogy leads subjects to believe and ultimately take part in processes leading to salvation. It is argued that in this Nip/Tuck episode, biopedagogy functions in and through Momma Boone's "confessions of the flesh", that is, confessions aimed at revealing her obese body so that it can be rescued, rehabilitated and saved. Momma Boone's salvation is shown to require three stages: first, the "confession" of obesity; second, the conversion to the "truth" of the "dominant obesity discourse"; and third, the codification of a "new life" for the obese subject. In the end, it is argued that since it is represented as abject, monstrous and out of control, Momma Boone's body is made to inspire fear and panic in so far as it provides constructed "evidence" regarding the consequences of the obese subject's failure to convert to the truth of obesity discourse.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".