Flexible sustainabilities: continuity and change in an MNCH project in Eastern Kenya
Bibliographic record
Abstract
In current global health parlance, sustainability is a key concern of funders and implementers alike, tied to growing concerns with accountability and, relatedly, the demand for precise measurement. Concerns over sustainability relate to vertical programming, fragmented care and short funding cycles. Communities who participate in successive programs may become disillusioned by the continual turnover and shifting priorities. In this thesis, I analyze the central role of program discourses, evidence production regimes and knowledge practices in the everyday life of a global health project to illuminate power imbalances that emerge within international partnership formations. Furthermore, within the context of a multi-partner maternal, newborn and child health (MNCH) project in Kenya, I illustrate how local communities engage with global health projects to ensure sustainability on their own terms. Methods: I conducted a 13-month ethnographic study of a MNCH and nutrition project in Taita Taveta County, eastern Kenya, following a project ethnography. Data collection took place between July 2014 and August 2016. Because the social dynamics of the project unfolded in multiple locales, I conducted my ethnographic fieldwork in Winnipeg, Canada, and in Nairobi and Taita Taveta, Kenya. In addition to participant observation, I undertook primary and secondary archival research to gain a deeper understanding of the history of development in Taita Taveta. I also conducted key informant interviews with university scientists and analyzed secondary qualitative data from the MNCH project. Data analysis occurred through an iterative process that triangulated the findings from the various sources. Results and Conclusions. Evidence is a central preoccupation in global health practice, with respect to 1) how scientists and program implementers attempt to meet funders’ demand for standardized, quantitative metrics and 2) how local people and communities position themselves to benefit from global health projects. Prolonged engagement with development projects cultivate “local intervention knowledges” that inform how people think about and engage with projects. I demonstrate how people in Taita Taveta strategically engage with discourses around sustainability, actively participating in the formation of the discourse while contesting and remaking the principles and activities of programs to better meet local exigencies. This I refer to as flexible sustainabilities.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".