SCIENCE AND CULTURE IN THE ENVIRONMENTAL STATE The Case of Reactor Layups at Ontario Hydro
Bibliographic record
Abstract
The widespread concern about the declining state of our physical environment is often accompanied by frustration about what to do to prevent or even reverse such deterioration. In the past, policy makers, legislators, and the general public have usually turned to sci-entists and scientific knowledge for answers. But recently, theorists and others have re-emphasized the importance of culture in understanding the environment. In this article, this culturalist critique of scientific knowledge is discussed and is then related to the decision by Ontario Hydro to lay up seven of its nuclear reactors. This situation is used to illustrate the continuing relevance of scientific knowledge for addressing environmental concerns. T he critical importance of culture for a proper understanding of our rela-tionship with our environment has been reinvigorated recently by sev-eral theorists. One major focus of debate has been the neglect of cultural knowledge in favor of technical, scientific knowledge in the formation of environmental policy. Szerszynski (1996) and others (Goldblatt, 1996) have pithily summarized this as the issue of “knowing what to do. ” How do we know what we should do about envi-ronmental problems? What body of knowledge can we use to resolve our current dilemmas, and how do we know that this body of knowledge is better than others?
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.033 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".