“No one said anything about driving in Film Preservation 101!”: The Lived Experience of Disability, Chronic Illness, and Neurodiversity in Moving Image Archival Education
Bibliographic record
Abstract
Disability, neurodiversity, and chronic illness are underrepresented in moving image archives. Lack of representation is felt within collections, users of archives, and most importantly for the purposes of this project, staffing. Archivists often need advanced level education to work in the field. Archival education is the first potential employment barrier. This project highlights accessibility gaps in North American moving image archival education programs by sharing the lived experience of disabled students, neurodivergent students, and students with a chronic illness studying and working within moving image archives. Through semi-structured interviews with students, alumni, and faculty of George Eastman Museum’s L. Jeffrey Selznick School of Film Preservation; NYU’s Moving Image Archiving Preservation program; UCLA’s former Moving Image Archive Studies program at UCLA and current MLIS Media Archival Studies specialization; and the Film and Photography Preservation and Collections Management program at Toronto Metropolitan University (formerly Ryerson University), students and alumni share their experiences from the application process until graduation. My own perspective is also included as a person with epilepsy who graduated from the program at TMU. Key theories in archival studies, archival representation, film preservation, disability studies, cinema studies, and archival accessibility practises inform contextualization and analysis of these testimonies to lived experience, with a constant awareness of the interdisciplinarity existing within these fields. Concepts emphasized throughout include the political/relational model of disability, care, affect, universal design, academic ableism, trauma-informed archival practise, archival silences/bias, “the archive” vs archives, the person-centered archive, and community archives. Students’ experiences are organized around three themes: institutions hosting the programs, the programs, and the archival space. I argue that to create more inclusive archival education programs and overall field, it is vital to engage with the lived experiences of disabled students, neurodivergent students, and students with chronic illnesses. Knowledge mobilization is at the center of this project. This dissertation not only highlights accessibility gaps in moving image archival education but also gives suggestions for how to correct them. Collaboration is necessary for archival inclusion; the student perspective is critical for inclusionary growth.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".