Institutional Ethnography as a Method of Inquiry for Criminal Justice and Socio-Legal Studies
Bibliographic record
Abstract
Institutional ethnography (IE) is a method of inquiry created by Canadian feminist sociologist Dorothy E. Smith to examine how sequences of texts coordinate forms of organisation. Here we explain how to use IE, and why scholars in criminal justice and socio-legal studies should use it in their research. We focus on IE’s analysis of texts and intertextual hierarchy, as well as Smith’s understanding of mapping as a methodological technique; the latter entails explaining how IE’s approach to mapping differs from other social science approaches. We also argue that IE’s terms and techniques can help examine the textual work undertaken in criminal justice and legal organisations, and reveal how people are governed and ruled by these organisational processes. In the discussion, we summarise how IE can productively contribute to criminal justice and socio-legal studies in the twenty-first century.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".