“You can only be happy if you’re thin!” Normalcy, happiness, and the lacking body
Bibliographic record
Abstract
Within the expanding field of fat studies, we have come to understand different ways that fat bodies have been scrutinized for their “deviance” from the anticipated norms of society. The author interrogates the role racializing assemblages play in categorizing and disciplining bodies based on humanism and its fixed sizeist-normative temporality. With the supported idealized form, the human template as well as racialized and fat bodies are distinguished as lacking under the criteria of deficit. Subsequently, the promise of happiness in the context of Whiteness and thinness is set up as a form of normative time that must be followed to gain access to the privilege of being human. An autoethnographic methodology is used to situate the author’s own experience as a fat, racialized woman grounded in theory. In the latter portion of the paper the author discusses the affirmation of the body and its materiality within a new materialist framework. What is of utmost importance to consider in affirmative politics is that one’s race and fatness can be accentuated and accepted as forms of difference informing and intermingling with each other and our surroundings as they are brought to the forefront.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".