Pests and partners: synanthropic insect roles in reindeer herding of North Asia and their implications for multispecies archaeologies
Bibliographic record
Abstract
Across Northern Eurasia, reindeer have long shaped the socio-cultural fabric of hunter-fisher societies. Today, descendant communities continue multispecies lifeways, forming symbiotic relationships within boreal ecosystems. Reindeer, regarded as animate persons, exist as both herded and wild partners. While the dynamics of these communities have been widely studied, the smallest actors in this system—namely insects—have remained largely overlooked, particularly in discussions of reindeer domestication and archaeology. Expanding ontological perspectives and engaging with new narrative approaches open avenues for recognizing other animate beings as co-constructors of social, economic, and cultural systems. Traditional hunter-herding practices in the West Siberian and Northwest Mongolian taigas offer insights into early human-reindeer cooperation, domestication, and their archaeological traces. This study employs a collaborative, multidisciplinary approach to examine how synanthropic insects—such as mosquitoes, midges, and horseflies—shape hunter-herder lifeways, despite their absence from the archaeological record. Fieldwork with Sel’kup, Khanty, and Tsaatan communities highlights the critical role of insects in herding and mobility patterns, influencing niche construction strategies. These case studies reveal new multispecies parameters that will enhance interpretations of the archaeological record.
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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 | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".