Bibliographic record
Abstract
In this paper we unpack how geographers have studied institutions, focusing specifically on institutional ethnography, often called ‘IE’. Sociologist Dorothy Smith is widely credited with developing institutional ethnography as an ‘embodied’ feminist approach. Smith studies the experiences of women in daily life, and the complex social relations in which these are embedded. Institutional ethnography offers the possibility to study up to understand the differential effects of institutions within and beyond institutional spaces and associated productions of subjectivities and material inequalities. We suggest that geographical scholarship on institutions can be enhanced and, in turn, has much to contribute to the broader interdisciplinary field on institutional ethnography, such as understandings of institutions that account for spatial differentiation. We argue that IE holds potential to enrich geographical research not only about a multitude of kinds of institutions, but about the many structures, effects, and identities working through institutions as territorial forces. In spite of recent interest by geographers, the broader literature on institutional ethnography remains under-engaged and under-cited by human geographers. Critical of this lack of engagement, we suggest that it has left a gap in geographical research on institutions. Our aim is to analyze and advance existing scholarship and offer this article as a tool for geographers thinking about employing IE. We develop a typology, categorized by methodological approach, to highlight ethnographic approaches to institutions undertaken by geographers.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.011 |
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".