Conducting fieldwork with Tarieng communities in southern Laos: Negotiating discursive spaces between neoliberal dogmas and Lao socialist ideology
Bibliographic record
Abstract
Abstract Based on research with ethnic minorities in Laos aimed at understanding how they cope with and negotiate political and economic ‘double domination’, this article examines the experiences of prolonged fieldwork in a remote Tarieng area in the Annam Range, southern Laos. After briefly reviewing Lao ethnographical policy and practice regarding ethnic minorities, I introduce the Tarieng people. I detail how I initially gained access to these local communities via long‐term engagement with a range of development project initiatives. Then, after eight years of conducting such fieldwork in a Tarieng area ‘below the radar of the state’, I managed to obtain official authorisations to continue research as a graduate student. In this new position, I accessed the field via different negotiations with central, provincial and local official bureaucracies. After detailing this process, back in the field I reveal my strategies to create a discursive space that has allowed me to access dissident Tarieng voices and agency. Finally, I highlight four central elements that have continued to shape my field research: language proficiency, working with research assistants, awareness of political relations and cultural sensitivity, and ethical concerns. These have emerged while the possibilities and constraints of political engagement with the Tarieng people are explored.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".