Funding Environmental Projects with Regulatory Prosecutions: Transparency and Accountability in Creative Environmental Sentencing
Bibliographic record
Abstract
This paper examines the use of creative sentencing orders which direct an offender to fund projects that facilitate objectives such as environmental research, education or remediation. Given the public interest character of sentencing for an environmental regulatory infraction, there is a surprising absence of transparency and accountability in the administration of these sentencing orders. The selection process for determining which projects are funded is shrouded in secrecy and is almost entirely a matter of discretion, which raises concerns that project recipients are chosen for reasons other than the environmental merit of their proposal. In our study of creative orders issued in Alberta, the majority of funding has been directed to post-secondary institutions and conservation funds who work closely with industry and government departments, while non-governmental environmental groups with established research, education and remediation programs appear to have received comparatively little funding. Good projects also need to be implemented, but insufficient details in sentencing orders and the absence of an oversight regime raises questions about whether project deliverables are ever met. This also creates risk that the funding is subsequently characterized as philanthropy by the offender. Transparency and accountability would be significantly enhanced by the enactment of legislation which establishes or appoints an agency to administer the funding and sets rules on matters such as public engagement in the project selection process, criteria used to evaluate project proposals, reporting on project outcomes, and enforcement.
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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 | 0.131 | 0.467 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| 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".