Uncovering Ableism in Martha Fineman’s Ontological Vulnerability and Resilience Theory: A Critical Disability Theory Perspective
Bibliographic record
Abstract
This paper begins with the premise that language contributes to the concept of disability and then engages a Critical Disability Theory (CDT) approach to scrutinize the rhetoric and language used by Martha Fineman in her work on ontological vulnerability and resilience. This paper will demonstrate that because the language used in Fineman’s ontological vulnerability theory and resilience model are highly susceptible to an ableist interpretation, her work inadvertently reinforces the notion that non-disabled people are fully human, while those with disabilities are insufficiently human. As a result, her scholarship may contribute to the prolonged oppression and othering of people with disabilities. The first part of the paper explains what CDT is, focusing largely on how ableism produces and sustains the co-constitutional concepts of disability and ability, and how CDT can be used as an approach for probing Fineman’s scholarship. The second part introduces ontological vulnerability and resilience theories, with a particular focus on Fineman’s work. The third part applies a CDT approach to uncover how Fineman’s language and rhetoric concerning vulnerability and resilience contribute to the construction of disability and ability. Finally, I highlight potential ways CDT scholars may reframe ontological vulnerability theory to overcome this issue. Keywords: Critical Disability Theory (CDT), vulnerability theory, Resilience Theory, Critical Feminist Theory, Martha Fineman
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".