Using Institutional Ethnography: University Audit Culture as People’s Textually Mediated Activities
Bibliographic record
Abstract
Institutional Ethnography (IE) is a feminist approach to research developed by English-Canadian sociologist Dorothy E. Smith (1922– 2022) in collaboration with colleagues and students (D.E. Smith, 1987, 1990a, 1990b, 1999, 2005, 2006b; Griffith and Smith, 2014; Smith and Turner, 2014a; Smith and Griffith, 2022). It largely focuses on how people's everyday lives are coordinated with others (Smith and Griffith, 2022, p 3), beginning in people's experiences and examining how they are organised by institutional texts and language, including audit processes. Confusingly, given the name, IE is not simply an ethnography of institutions, but rather provides a comprehensive ontology of the social, concepts to help describe the dynamics of the social, and a methodological framework for doing research. IE has become an expansive interdisciplinary, international field, with researchers taking up Smith's work and the IE approach in vastly different ways, as I discuss in my thesis (Murray, 2019, pp 43– 64), and as shown through IE edited collections (Campbell and Manicom, 1995; Frampton et al, 2006; Smith, 2006b; Griffith and Smith, 2014; Smith and Turner, 2014a; Reid and Russell, 2018; Lund and Nilsen, 2020; Luken and Vaughan, 2021, 2023). As such, it is difficult to succinctly describe and so for those who are unfamiliar with IE, I recommend Smith and Griffith (2022) and Smith (2005), which provide a comprehensive overview of the approach.
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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".