Observing Neurodiversity, Observing Methodology: Ethnography in Pandemic Times
Bibliographic record
Abstract
Ethnographic researchers have long relied upon observation as a powerful means to learn about social relations. This paper discusses research observation that was conducted as a part of an institutional ethnography (IE) investigating how people use the language and ideas of neurodiversity across different settings. While our research protocol initially called for ethnographic observation to take place at in-person events in Southern Ontario, our approach needed to be re-formulated with the switch to online events during the COVID-19 pandemic. After the shift to online-only spaces, a total of 52 sessions at 7 online events related to neurodiversity or autism were observed by a team of 5 researchers: these events were no longer geographically restricted but were officially “hosted” by institutions in Canada, the US, and the UK. This paper reflects upon the challenges and opportunities we encountered as we conducted observations in digital spaces, including our experiences of navigating the “chat” feature. We discuss the need to analyze the format as well as the content of online events, and present findings on how neurodiversity appeared in these social spaces. Finally, we consider the implications of this research for people who are conducting ethnographic observation in an increasingly online world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.077 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".