Remaking and Living with Resource Frontiers: Insights from Myanmar and Beyond
Bibliographic record
Abstract
Myanmar, a nation situated between India, China and Southeast Asia, has long histories of colonialism, violence, and resource extraction. This special issue introduction, written in the midst of Myanmar’s 2021 military coup and the COVID-19 pandemic, offers two critical and feminist interventions – ‘remaking’ and ‘living with’ – to understand the contested and embodied political geographies of extractive resource frontiers in Myanmar. ‘Remaking’ focuses on the long roots of resource frontiers, underscoring the historical and spatial processes through which Myanmar’s plural authorities have restructured diverse territories for accumulation and extraction from the pre-colonial period to the recent ‘democratic transition’. ‘Living with’ resource frontiers bring attention to people’s everyday lives, and why and how they adapt, resist, comply, suffer and profit from resource frontiers. In bringing together a diverse set of literatures with original empirical research, the articles in this collection offer analyses of Myanmar’s pre-coup period that inform contemporary post-coup politics. Together, they demonstrate the material, affective, and embodied nature of resource frontiers as they are (re)made and lived with – in and beyond militarised spaces like Myanmar.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".