Transnational Institutional Ethnography: Tracing Text and Talk beyond State Boundaries
Bibliographic record
Abstract
Purpose: In this article I provide a rich account of how I utilized and critically applied the research strategy of institutional ethnography to investigate transnational processes of legislative standardization. The text at the center of this inquiry is a model law that was funded by the United States Agency for International Development (USAID) to create omnibus HIV/AIDS laws across West and Central Africa (2005-2010). Expanding upon the sociological approach of institutional ethnography, my research method is best understood as a transnational institutional ethnography (TIE). This article provides a case study of TIE for those interested in ethnographically exploring transnational processes across diverse institutional settings. Design/Methodology/Approach: The complex legislative process being investigated was made visible through the use of participant observation, archival research, textual analysis, and informant interviews with national and international stakeholders (n=32). This research strategy involved ethnographic data collection in Canada, the United States, Switzerland, Austria, South Africa, and Senegal (2010-2011). Findings: In this article a methodological discussion is offered focusing upon the everyday actualities of conducting transnational research in diverse environments, including cafes, conferences, courtrooms, and activist gatherings. I provide an account of (a) the complex institutional sites from which a research problematic may emerge; (b) challenges and opportunities when conducting interviews and identifying informants; (c) the importance of accounting for matters of geography and interview location in one's study design; and (d) the work of knowing where to look, what to read, and who to talk with during the iterative process of research and discovery. Originality/Value: Moving beyond state-based organizational relations, a focus which is predominant in most institutional ethnographies, in this article I explicate the research process undertaken to ethnographically interrogate complex processes of transnational social organization and translocal text-mediated relations. Methodological insights and lessons learned regarding the experience of conducting transnational ethnographic research are provided.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".