The Paleoenvironmental Humanities: Climate Narratives, Public Scholarship, and Deep Futures
Bibliographic record
Abstract
In his keynote, Felix Riede explores how archaeology might contribute to the environmental humanities, an arena he has recently entered with his new position at Aarhus University.Riede reviews the development of this relatively new interdisciplinary engagement and its contribution to climate change discussions.He suggests that we all should be involved in this conversation no matter our particular archaeological theoretical orientation.I particularly appreciate his position on the importance of narratives, his argument that deep human pasts must be foregrounded in current discussions of climate, and his view that strong, empirical evidence (particularly the kind produced in environmental archaeology) should be emphasized in these ongoing interdisciplinary discussions.In this commentary, I consider how the environmental humanities could contribute to discussions of the climate crisis beyond our disciplinary spaces and our university settings.My response stems from non-European contexts.I am based in an anthropology department at a Canadian university, and conduct archaeological fieldwork in the Bolivian Andes, a region that is undergoing rapid climate-based changes.Concerns about climate futures permeate my conversations with Canadian students, where my teaching about the dynamics of past landscapes has gained new relevance in recent
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".