Donner la parole au terrain dans la recherche environnementale : une approche transdisciplinaire pour l'étude de cas de la route de l'Alaska
Bibliographic record
Abstract
Over the past two decades, environmental humanities and social sciences have emphasized the agency of non-humans and the need to move beyond anthropocentric research frameworks. While this shift has gained wide acceptance conceptually, concrete methodologies to integrate human and non-human voices in empirical research remain scarce. Existing studies on permafrost thaw underline the vulnerability of infrastructures and communities in the circumpolar North, but often treat soils, ice, or infrastructures as background variables rather than active agents. Our project asks: How can « ground » non-humans (e.g., permafrost, soils, infrastructures) be integrated as full actors in environmental research? More specifically: What methodologies allow us to capture the temporalities and agency of these non-humans alongside human narratives? How can this approach reframe socio-ecological transformations in Arctic and Subarctic contexts, particularly along the Alaska Highway (Yukon, Canada)? We propose a crossed methodology combining: (1) Anthropology and Ethnography: interviews and observations with local residents, First Nations, and highway workers, to capture lived experiences of environmental change. (2) History and Archives study: construction reports, maps, photographs (1940s–present), and Indigenous lexicons to situate transformations in historical depth. (3) Geosciences and Geology: monitoring data of permafrost, soil analyses, and geomorphological surveys, interpreted as “spokespersons” of non-humans. This triangulation enables the production of multi-temporal narratives in which human and non-human actors are co-authors of history.
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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 | 0.188 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.027 | 0.028 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".