Chapter 1 Activating Cosmo-Geo-Analytics: Anthropocene, Arctics and Cryocide
Bibliographic record
Abstract
In our introduction we laid out several concepts we feel are pertinent to understanding environmental processes in general: the need to recognize multiple worlds and the positions humans actors occupy within them; the urgency of acknowledging that expert knowledge emerges in many forms and that this knowledge may be communicated in many ways; and, finally, how peoples across the globe are experiencing and responding to uncertainty, unpredictability and precarity invites a continuing consideration of 'risk' as an analytic.The present chapter draws upon those ideas but turns readers' attention more specifically to issues that influence our contributors' analysis of Arctic conditions.In this we consider the Arctic as a specific ecozone which has generated a significant body of environment-related research, much of it subject to the questions of voice we introduced in the Introduction.We also examine the Circumpolar North as a particular cosmo-political zone which continues to register the nineteenth-century colonial footprints of Russia, the US, Canada and Denmark; these in turn have generated innovative and persistent pushback on the part of local residents across the region.We then explore briefly the extent to which it remains a global hotspotpolitically, economically, and ecologically -with tensions between those who want to exploit its non-renewable resources and those who focus more on the protection of its renewable resources which carry moral and spiritual weight in terms of interspecies sociality.The Anthropocene as a concept mobilizes so many of these issues
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".