Bibliographic record
Abstract
For environmental thinkers of different stripes—from techno-scientific ‘eco-pragmatists’ to climate activists—the concept of human failure—whether psychological, moral, or spiritual—is at odds with the language of hope needed to generate meaningful action. As Clingerman’s work on geo-engineering attests, failing to adequately meet the challenge of climate and ecological crisis is frequently expressed as a state to be overcome, through divine or human techno-scientific intervention. Against such a view, I want to propose failure as generative of environmental ethical thinking, particularly in times of mass extinction and irrecoverable ecological devastation. I do this by linking failure with two concepts that have become important to environmental humanities scholars: first, the concept of mourning as an ethical disposition (via the philosophies of Benjamin, Freud, and Derrida) that can foster more just, compassionate and sustainable ways of living. Second, inviting further interaction with Clingerman’s work, I propose to link failure to the concept of environmental hermeneutics, by understanding language itself as a sign of human failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".