Bibliographic record
Abstract
Contemporary scholarship regards the acknowledgment of harm as an ethically necessary precondition for work on the environment. In this article, I show that the admission and subsequent management of harm have long been central to racial and colonial projects. To do so, I trace a logic of what counts as tolerable damage and what is thought to be able to be repaired in environmental assessment reports produced for the Alberta tar sands. What I find in these documents is that anxiety over complicity with historical damage leads to fantasies of reparability. In analyses of the political culture of the tar sands, I argue that conceding damage is better understood as an attempt to manage the appearance of violence and reinterpret its history. In the different examples on which I focus, responsibility for harm is performed. By making impacts legible and detailing plans to address them through mitigation, compensation, or replacement, resource extraction companies engage in fantasies of repair and admissions of destruction. This article works to theorize what function such gestures serve and how they contribute to perceiving the environment as something that must be managed. I show that its function is to describe the nature of loss along with a theorization of its reality.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.086 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".