Institutional Ethnography and Surveillance Studies: An Outline for Inquiry
Bibliographic record
Abstract
Institutional ethnography (IE) is a method of inquiry that problematizes social relations at the local site of lived experience, while examining how sequences of texts coordinate consciousness, actions, and ruling. I argue that institutional ethnography is a critical method of inquiry useful for revealing and reshaping the textual organization of surveillance. Central issues regarding institutional ethnography as a method of inquiry, including standpoint(s), texts and institutions, interviewing, and mapping, are discussed. Researchers studying surveillance would benefit from engaging with the method of institutional ethnography during future research because IE has a unique ability to explicate how surveillance subjects are linked to and managed by discursive, managerial, and professional forms of power.
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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 | 0.097 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.010 | 0.056 |
| Scholarly communication | 0.022 | 0.035 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".