Something is happening: encountering silence in disability research
Bibliographic record
Abstract
Purpose Often, researchers view silence as antagonistic to equity-aimed projects. Because verbal, written, and textually agentive communications are presumed to be the most valid qualitative-research data, moments of silence are under-analyzed. Yet, we argue that silence holds meaning as data and that it is a valid, rich form of communication. Design/methodology/approach Through this reflective analysis of silence, we invite readers to reconceptualize silence in research from a critical disability-research perspective with emphasis on crip willfulness. We introduce silence as an interpretive, agentive and relational gesture. Findings We attend to silence as necessary in all research because it helps researchers excavate able-bodied expectations about communication in qualitative-data-collection practices. Originality/value We demonstrate that silences in research can be an interpretive, relational, and agentive gesture that can teach us about taken-for-granted assumptions about research practices. Revisiting our research encounters with this framing of silence informed by critical disability studies allows us to question how traditional social science research methods value some modalities of expression over others. Rather than viewing silence in research as moments when nothing happens, we show that silence indicates something happening and is valid data.
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 | 0.114 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.027 | 0.109 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.011 |
| 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".