Bibliographic record
Abstract
Reflections on insider research, user-led research, and lived experience research, as well as the question of positionality, have long been part of the scholarly conversation in various fields, such as Anthropology and Disability Studies. The present study provides a map of insider research in media accessibility through a literature review and discusses the results of 11 semi-structured interviews with insider researchers working on (media) accessibility and neighbouring fields. Their reflections on positionality in insider research, the benefits and challenges of this approach, their practices in the insider-outsider continuum, and the (in)accessible research processes which they encounter are presented. Researchers reflect on positionality in a nuanced manner, highlighting topics such as visibility and self-reflection, but also stigma and performativity. Experiential closeness to the topic being researched, heightened empathy, and legitimation of insider researchers’ and participants’ knowledge are highlighted mostly as positives elements, granted that researchers are offered psychological support. For most of the researchers, the risks (emotional impact, unclear role of the researcher, if they are well acquainted with the participants, etc.) can be tackled or resolved (through epistemological reflection, through collaboration, through psychological support, etc.) and the general argument is that the benefits of insider research outweigh the disadvantages. Through the researchers’ experience, we argue for greater agency among insiders in the research of media accessibility.
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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 | 0.050 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".