An Institutional Ethnography of Women Entrepreneurs and Post-Soviet Rural Economies in Kyrgyzstan
Bibliographic record
Abstract
The overarching problematic of this study is to understand how initiatives developed by international non-governmental organizations (INGOs) come to organize processes of economic and social 'development' in Jerge-Tal village, located in a remote mountainous region of Kyrgyzstan. My objective was to examine how courses developed and delivered by Women Entrepreneurs Support Association (WESA) coordinate with the actual needs, capacities, and work processes of women entrepreneurs and the broader contexts in which they live and work. My original contribution to knowledge is an account of how people's work processes are drawn into and coordinated by a set of relations that, whether intentional or not, preclude dialogic interchanges across a sequence of interrelated activities that link my own academic work, the institutions and work practices of development workers (both local and international), the goals and practices of different levels of governance, and the efforts of women entrepreneurs in local sites where 'development’ actually happens. I used Institutional Ethnography (IE) as a framework of inquiry to investigate how development agendas aimed at improving the well-being of women are coordinated at institutional and local levels. Training programs for women entrepreneurs are part of a strategy developed by Western gender specialists concerned with how to address the problem of women's social and economic marginalization. As such, they are tied into an international development programming complex wherein concerns with women's well-being are articulated through institutional processes (such as accounting systems, accountability systems, and computerized technologies) which produce definitions of gender, establish gender mainstreaming programs and policies, and assess effective implementation and compliance with these processes. This study contributes to better understanding how such processes operate. The insights provided offer a starting point for developing a body of knowledge about local development processes that is empirically informed, politically useful, and, at least to some extent, locally produced. This kind of knowledge is politically useful to the local peoples who have contributed to it, but also to the institutions that study and serve them (or fail to serve them), and those seeking to better specify what concepts like colonization, capitalism, and transformation mean in the post-Soviet Kyrgyz context.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".