Excavating One Anthropologist's Investigation into Conservation‐Based Conflicts in Northern‐Most Mongolia—A Brief Exposition
Bibliographic record
Abstract
This photo essay addresses conservation‐based conflicts that stem from the Tengis–Shishged National Park and are imbricated in broader institutional processes, drawing on seven months of eth(n)ography conducted in 2014–2018 among Dukha hunter‐gatherer reindeer pastoralists in northernmost Mongolia. I elaborate some of the institutional, social, and economic dimensions of these conflicts and expose a history of my own multifaceted learning process in and beyond the field. Excavating this history serves as a means to convey topical knowledge and affords a deeper appreciation of how learning takes place. Dispositions emerge in mutually generative counterpoint with experience(s), skills, and inclinations through various stages of anthropological practice, namely (though not exclusively) fieldwork. I do this hoping to bring attention not only to the existence of conflicts over the national park and their effects on Dukha people, but also to some of the subtleties that make these conflicts and effects so contentious. In doing so, I highlight the salience of anthropological practice and dispositions and the time and phasal oscillations that go into them for understanding and pragmatically engaging with contemporary social issues, whether they be localized, systemic, or some multi‐scalaramalgamof the two. I briefly introduce the term ‘eth(n)ograph/y/ic,’ which develops van Dooren and Rose's (2016).notion of more‐than‐ merely‐human “ethography” by combining ‘ethnos’ and ‘ethos.’ I develop this concept because I wish to emphasize how anthropological attention to ethea often exceeds focus on any one distinct ethnicity and, indeed, species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.002 | 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".