Bibliographic record
Abstract
Disabled people constitute the "largest minority in the world" (United Nations, 2006). As such, a study of contemporary memory-making practices around disability can provide a crucial lens through which to engage with ongoing issues of socioeconomic and cultural marginalization. This article examines two digital memory projects which showcase the life stories and creative outputs of people with disabilities: the Museum of the Person USA (Bloomington, US) and Envisioning New Meanings of Disability and Difference (Toronto, Canada). I argue that a close analysis of these projects illustrates a current tension between autobiographical, self-expressive memory narratives and those orientated by wider sociopolitical claims. Significantly, gendered aspects of disability discourses also work to unsettle the boundaries of what we understand the "therapeutic" and "democratic" to be. At present there is scant research taking people with disabilities' own testimony and experience as its core sources. The rise of networked technologies and the opportunities this creates for marginal memories to be publicly shared and witnessed therefore provides a compelling context through which to examine the mediation of disability stories online, and the ways in which these memories can act as tentative resources for social justice claims.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.080 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| 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".