Bibliographic record
Abstract
What if participants with bodily dispositions, such as deafness or blindness, took part in video conference sessions?In deaf and/or blind disability communities, data intensive digital environments are experienced as vulnerable and it has been argued, since the late 1990s onwards, that they cannot be treated as given, culturally inclusive spatial surroundings (Kitchin 2000; Goggin and Newell 2000).Spatial environments can be identified as a "dis/ability complex" (Goodley 2014) based upon experiences of power and violence in various disability communities and as situated social constellations under which both inclusive bodily abilities are negotiated and in which exclusionary mechanisms of disabling become reproduced.Research on disability communities and digital spaces during the Covid-19 pandemic (Yong et al. 2025) in particular has shown that digital solutions can be seen as a non-adequate factor to coping with isolation and loneliness.Digital participation by sensory and bodily means is limited because of the dominance of visuality and aurality that are encoded into technological devices, and because of the required touch-, movement-, and gesture-based competences in handling them.Even though universal design is an established field concerned with disability communities, these are not envisioned for plural sensory and bodily dispositions when it comes to digital devices and software environments (Hamraie 2017).To feel oneself as a "participant" (Kelty 2019) in a video conferencing environment, thus, always requires complex and resource-intensive media translations, infrastructural reconfigurations, and not mere promises of inclusivity.These circumstances become particularly pressing in disability communities because sensory and bodily participation is organized based upon a lack of resources and negotiations for corporeal standards in digital infrastructures that have not taken place in the context of diversifying access to society.This chapter argues for the consideration of access work in digital disability communities; I wish to call for a cultural repair of the promises of inclusivity in digital spaces in order to argue for greater acknowledgment and visibility of this mandatory work and to inquire into modi of participation for a future common ground of action.In order to realize this speculative approach, I wish to utilize the method of pre-enactment, as an ethnographic way of knowledge production, in order to trace
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".