Interrupting the Telos: Locating Subsistence in Contemporary US Forests
Bibliographic record
Abstract
People continue to hunt, fish, trap, and gather for subsistence purposes in the contemporary United States. This fact has implications for forest policy, as suggested by an international convention on temperate and boreal forests, commonly known as the Montréal Process. Three canons of law provide a legal basis for subsistence activities by designated social groups in Alaska and Hawaii and by American Indians with treaty rights in the coterminous forty-eight states. A literature review also presents evidence of such practices by people from a variety of ethnic backgrounds throughout the nation. Teleological notions of development espoused by both neoliberal and Marxist scholars suggest that subsistence activities should not persist in a First World setting except as failures of the officially sanctioned economic system. However, alternative economic perspectives from peasant studies and economic geography offer a conceptual framework for viewing at least some subsistence activities as having a logic and values outside of, if articulated with, market structures. Meeting the Montréal Process goal of providing for subsistence use of forests will require research focused on local practices and terms of access to resources as well as their relationship to state and capital processes. We outline the basics of a research agenda on subsistence for an emerging First World political ecology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".