Studying Environmental Regulation in Laboratory Environments
Bibliographic record
Abstract
From Introduction:\n\n"Members of the Department of Economics at McMaster University are participating in a major eco-research programme at the University which is funded by the Government of Canada and which focuses on the restoration of the Hamilton Harbour and the harbour eco-system. The McMaster research programme encompasses researchers from the humanities, social sciences, physical sciences, health sciences, and engineering. While much of the research is discipline oriented (such as engineers studying water flows in the harbour and how this effects the distribution of toxic substances and biologists studying the effects of toxic substances on bird populations), a considerable amount of inter-disciplinary work is being undertaken (such as psychologists, sociologists, and economists joining to prepare surveys to evaluate attitudes toward the environment and to acquire contingent valuations of different environmental projects).\n\n"Laboratory methodology is becoming established as an effective tool for evaluating economic theories about individual and market behaviour and for testbedding institutions designed to implement regulatory policies (Plott, 1991). Experimental economics laboratories are established in the United States, Europe, the United Kingdom, and Japan. As part of the eco-research programme, McMaster University has established the first dedicated experimental economics laboratory in Canada. Work is proceeding on the study of a pollution emission permit trading scheme which has been proposed for nitrous oxides and sulfur oxides in Canada (Godby et al., 1994, 1995; Mestelman at al., 1993; Muller and Mestelman, 1994). Research is directed towards studying the benefits of permit banking, the trading of permit entitlements, market uncertainty, and industry concentration (Brown Kruse et al., 1995). In addition to studying market based regulation schemes, researchers at the McMaster Experimental Economics Laboratory are studying voluntary allocations by individuals of resources to group goods (such as environmental clean-up) and alternative forms of collective decision-making for determining environmental standards (Chan, Godby et al., 1994a, 1994b; Chan, Mestelman et al., 1995a, 1995b)."
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".