Bibliographic record
Abstract
This article discusses various ways in which conventional discourse on sustainability fails to acknowledge the distributive, political, and cultural dimensions of global environmental problems. It traces some lineages of critical thinking on environmental load displacement and ecologically unequal exchange, arguing that such acknowledgement of a global environmental `zero-sum game' is essential to recognizing the extent to which cornucopian perceptions of `development' represent an illusion. It identifies five interconnected illusions currently postponing systemic crisis and obstructing rational societal negotiations that acknowledge the political dimensions of global ecology: 1) The fragmentation of scientific perspectives into bounded categories such as `technology', `economy', and `ecology'. 2) The assumption that the operation of market prices is tantamount to reciprocity. 3) The illusion of machine fetishism, that is, that the technological capacity of a given population is independent of that population's position in a global system of resource flows. 4) The representation of inequalities in societal space as developmental stages in historical time. 5) The conviction that `sustainable development' can be achieved through consensus. The article offers some examples of how the rising global anticipation of socio-ecological contradiction and disaster is being ideologically disarmed by the rhetoric on `sustainability' and `resilience'.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".