Why so slow?: Tri-level government's impact on environmental solutions in Nova Scotia
Bibliographic record
Abstract
Everyday, the Canadian government makes decisions through the interaction of the federal, provincial and municipal government. Although most citizens are only concerned with the outcome of these interactions, it it important to evaluate the process through which these decisions emerge. Typically, the process is mandated by legislation designating jurisdiction to a particular level of government. In the case of environmental legislation, the jurisdiction becomes less clear. This thesis will attempt to examine the interaction of tri-level governments through two environmental remediation projects in Nova Scotia in order to determine what factors slow down the process. Through an analysis of two separate environmental initiatives, the Halifax harbour and the Sydney tar ponds, it is obvious that there are three main features of the inter-governmental relations that are affecting the timely completion of these projects. The factors that impede progress are the jurisdictional duplication and vagueness on environmental issues, competing goals and objectives of the stakeholders, and cost-sharing and funding strategies in developing these projects. Analyzing these factors within specific cases gives insight into the real decision process behind environmental issues. With the environment weighing more on the Canadian conscience today, it is more important than ever to ensure an effective framework for the interaction of the municipal, provincial and federal governments.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".