Inviting the Infinifat Voice to the Fatshion Conversation: An Exploration into Infinifat Identity Construction, Performance and Activism
Bibliographic record
Abstract
This thesis explores how self-identified “infinifat” people, defined as those larger than a US woman’s dress size 32, access commercially available fashion and how their lack of access to clothing shapes the performance of their fat identity. Through semi-structured interviews with infinifat subjects and a secondary discourse analysis of “superfat” narratives in popular texts, this research finds that a lack of clothing options reinforces the stigma and discrimination experienced by those at the largest end of the fat spectrum. Particularly, the lack of clothing available to superfat and infinifat people restricts access to social spaces and economic opportunities. While this research draws attention to ways in which my infinifat participants are “hacking” fashion to suit their needs and using social media to advocate for inclusion, the fashion industry’s unwillingness to create clothing options for superfat and infinifat people, supports the perception that being really fat is really bad.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".