Bibliographic record
Abstract
As the window of opportunity to limit global average warming to 1.5 °C above pre-industrial levels is narrowing, the impacts of climate change are already being experienced around the world. No longer of merely theoretical interest, the issue of ‘loss and damage’ has become central to climate politics. Against this backdrop, old concepts of responsibility and wrongful harm are being revisited. Boran (2017) proposed moving away from an interactional conception of harm to an architectural one. The former supports the widely shared view that wrongful harm results from actions. The latter turns the spotlight on complexity and social practices. In response to critical appraisals, this short essay revisits key components of an architectural conception of harm. An architectural approach is not an analytic tool to answer the epistemic challenges in singling out those who are causally responsible as a discrete problem. It is an encompassing theoretical framework drawing a picture of a world where vulnerability is inseparably tied to a complex web of social, political, and institutional interrelations. Wrongful harm resulting from climate impacts is inextricable from the constructed environment and everyday practices forming a complex web of social and political interconnections.
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 | 0.044 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.133 |
| Scholarly communication | 0.019 | 0.057 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.042 | 0.060 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".