Practicing Dorothy Smith’s feminist alternative sociology in the criminal justice system: A case study
Bibliographic record
Abstract
This paper illustrates, through a case study, the potential of Institutional Ethnography (IE) as a research approach to study and transform the criminal justice system. IE is a research approach developed by the Canadian sociologist Dorothy E. Smith that focuses on how texts and language coordinate people’s lives across different contexts. In resisting prescriptive theories, Smith (1987) defined IE as an ‘alternative sociology’, a combination of an innovative feminist-Marxian critique of the sociological tradition and insights from ethnomethodology. IE examines institutional forms of social organisation, placing texts as replicable material objects at the centre of its analysis. IE analysis begins with people’s everyday/everynight embodied experiences, interactions, and discursive relations of ruling. However, rather than generalising from individuals’ experiences, IE examines how individuals are organised by already generalised texts and discourses. Initially described as a sociology for women, IE has since been used by, and for, people of all genders. The application of IE to the field of the criminal justice system has expanded over the last two decades. Following early IE work on homophobic policing of gay men and on domestic violence, the need for IE research and impact in this field has been clearly established. This paper utilises a research study on domestic violence and institutional biases conducted in three countries as a case study to demonstrate how IE can be applied. The analysis highlights the gaps between victims’ needs and institutional discourses and procedures and the practices of frontline workers. By recognising these ‘disjunctures’, IE provides empirically grounded suggestions for effective processual change. This paper also emphasises the methodological versatility of IE. In conclusion, this paper demonstrates how IE aligns with a pluralistic sociological perspective that embraces anti-positivist, feminist, action-oriented qualitative research, and how IE can help scholars challenge dominant ruling discourses within the criminal justice system. Keywords: institutional ethnography, gender, criminal justice system, social research methods, domestic violence.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.027 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".