Repurposing the (Super)Crip: Media Representations of Disability at the Rio 2016 Paralympic Games
Bibliographic record
Abstract
Mega-events attract ever larger media audiences, and the 2016 Rio Paralympics were no exception. As audiences grow, media coverage extends to ever more varied domains, which are themselves then colonised by an increasing range of discourses. One of main discourses to develop since the early 2000s has been that of the so-called supercrip, one which challenges the notion of “impairment” often connected with disability by foregrounding the para-athletes’ triumph over adversity, celebrating instead their courage, grit, and perseverance leading to athletic success and personal and increasingly national prestige. In this article, we analyse the continuing importance of the supercrip discourse in coverage of the Rio Paralympics but also move on to highlight its tactical alignment with other—both competing and complementary—discourses of nationalism, sexualisation, militarisation, and celebritisation. We analyse textual and visual manifestations of these discourses using both critical discourse analysis and Foucauldian discourse analysis. We conclude by paying particular attention to the increasing visibility of discourses which, while acknowledging the potentially positive role of the supercrip discourse in focussing on athletic success, repurpose that discourse by foregrounding instead the day-to-day experiences of belittling misrepresentation and neglect, including political neglect.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".