Academic Coverage of Online Activism of Disabled People: A Scoping Review
Bibliographic record
Abstract
Disabled people need to be activists given the many problematic lived realities they face. However, they frequently encounter obstacles in traditional offline activism. Online activism could be a potential alternative. The objective of this scoping review is to examine the extent and nature of the coverage of disabled people in the academic literature that focuses on online activism. We searched the abstracts in Scopus, Web of Science, and the 70 databases in EBSCO-HOST for the presence of 57 terms linked to online activism or online tools or places for online activism, which generated 18,069 abstracts for qualitative analysis. Of the 18,069 abstracts, only 54 discussed online the activism by disabled people. Among these 54 relevant abstracts, only one contained the term “Global South”. No relevant abstracts were found that contained the terms “Metaverse” or “Democrac*” together with “activis*”. Only two relevant abstracts contained the phrase “digital citizen*”. Out of the 57 terms, 28 had no hits. The thematic analysis identified 24 themes: 6 themes in 30 abstracts had a positive sentiment, 7 themes in 30 abstracts had a negative sentiment, and 11 themes present in 23 abstracts had a neutral sentiment. There were three main themes: the positive role and use of online activism; the technical accessibility barriers to online activism; and the attitudinal accessibility problems arising from ableist judgments. The intersectionality of the disability identity with other marginalized identities and the issue of empowerment were rarely addressed, and ability judgment-based concepts beyond the term’s “ableism” and “ableist” were not used. The study underscores the necessity for further research given the few relevant abstracts found. The study also indicates that actions are needed on barriers to online activism and that examples for best practices exist that could be applied more often. Future studies should also incorporate a broader range of ability judgment-based concepts to enrich the analysis and to support the empowerment of disabled activists.
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 | 0.025 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".