Institutional Ethnography, Critical Discourse Analysis, and the Discursive Coordination of Organizational Activity
Bibliographic record
Abstract
Abstract Institutional ethnography (IE) is a social ontology pioneered by Dorothy Smith, the Canadian feminist-sociologist. Conceptualizing discourse as social relations that are organized by the activities of people and are empirically investigable, IE has been increasingly employed by researchers outside of sociology in fields such as education and health. The goal in these cases has often been to explicate the effects of power flowing through textually mediated discourses that work to reconfigure local practices to align with official policy mandates. Yet the discourse analysis performed in much IE to date has not paid close linguistic attention to the way specific actors utilize texts in an active appropriation of what Smith calls the “ruling relations” constituting official discourses. Using data from an IE of student equity practices in Australian higher education, this chapter illustrates how a Fairclough-inspired critical discourse analysis (CDA) of the “orders of discourse” assembled within a relay of university and government texts is able to provide useful analytical purchase on how equity policies are actively appropriated within a university outreach practice. It demonstrates how the accomplishment of student equity outreach involves the hybridizing of equity and excellence discourses in ways that bolster the dominant position of an Australian university. This working together of distinct IE and CDA approaches offers possibilities for more nuanced accounts of individual and collective agency in the process of semiotic and social change.
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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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| 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".