<i>Human, all too human</i> ? Anthropocene narratives, posthumanisms, and the problem of “post-anthropocentrism”
Bibliographic record
Abstract
What role do contemporary narratives and counter-narratives play in policy regarding the Anthropocene crisis? Given the centrality of the anthropos in the Anthropocene, what conditions might make possible a “post-anthropocentric” or “non-anthropocentric” narrative? Tracing the production of both dominant and counter-narratives, the struggle for narrative power centers the role of the anthropos in the Anthropo cene. The standard narrative—“strong anthropocentrism”—maintains humanist assumptions relating to the “control” and “cultivation” of the non-human. In contrast, counter-narratives, from both alter-humanist eco-centric and post-humanist positions, attempt to de-center human-centrism toward more egalitarian responses to the Anthropocene. Despite these attempts at de-centering human spheres of influence, this article argues that these counter-narratives maintain a “weak anthropocentrism,” given their maintenance of human volition and intentionality. The production of “post-anthropocentric” or “non-anthropocentric” narratives of the Anthropocene crisis would require speculative moves beyond the human: toward human abolition and disconnection.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".