Fields in Motion, Fields of Friction: Tales of "Betrayal" and Promise from Kangra District, India
Bibliographic record
Abstract
Over a period of five decades, Kangra District, located in the mountainous northern Indian state of Himachal Pradesh, has been continually si(gh)ted within different development imaginaries that have evolved at particular configurations of scale and time and been given shape within a succession of international bilateral projects. These development flows into the region have in turn fostered a plethora of competing institutions and practices, contributing their own sometimes divergent flows within an inherently mobile and “developmentalizing” terrain. While this dynamic and multi-textured terrain offers rich opportunities for partnerships forged across disparate sites, there is, I argue, a need to revisit “collaboration” as a key feminist tool for facilitating social justice and change. Widely lauded for its empowering, equalizing and transformative potential by feminist scholars, collaboration is also viewed prescriptively in terms of “success” and “failure.” Consequently, “strategies and solutions” are sought to negotiate its minefields and to resolve, often futilely, the friction that repeatedly erupts within them. In this paper, I suggest a re-viewing of friction as a valuable methodological frame within feminist collaborative research and praxis. In place of the prevalent emphasis on containing and resolving friction generated at border crossings, I contend that feminist-oriented “location work” that engages with friction and follows its routes through the fluid and fertile space of a “developmentalizing terrain” can provide promising avenues and detours for empowerment and social justice. Drawing on Tsing’s (2005) discussion of the creative role of friction across global connections, I reflect on some of the ways in which it played out as a creative source of production, interruption and mutation within two of my collaborative ventures in Kangra. In doing so, I demonstrate how an attention to the sometimes unanticipated and diversionary routes that are generated by friction within collaborative efforts, and the vistas of “betrayal” and promise that they reveal, offer valuable insights for encounters at the interface of feminist praxis, anthropology and development practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.056 | 0.049 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".