SCIENCE ADVICE IN ENVIRONMENTAL POLICYMAKING: AN EXAMINATION OF INSTITUTIONAL BARRIERS IN THE CANADIAN FEDERAL GOVERNMENT
Bibliographic record
Abstract
Evident in many Canadian federal government and international publications is the recognition of the underlying importance that scientific knowledge has in evidence-based policymaking. Despite this recognition, academic and empirical research suggests that there are a number of barriers preventing the utilization of science in policy. This thesis examines such barriers in the context of environmental policymaking in the Canadian federal government. It uses a collection of multidisciplinary literature, Canadian federal government documents and interviews with officials from the Canadian federal departments of Natural Resources Canada, Environment Canada, Fisheries and Oceans Canada and Health Canada, to then make generalizations about the organizational barriers affecting the federal government at large. The main conclusion will show that while the barriers pertaining to Knowledge Producers and the Representation of Knowledge are important, the main barriers preventing the use of environmental knowledge in policy are with respect to Science-Policy Linkages. For example, departments have made major strides to improving the communication channels between science and policy domains, either through specific councils, boards, or even the creation of new branches and sectors of certain departments. These findings suggest a key implication: to overcome the most prevalent barriers, and the barriers that arguably are most significant, considerable focus must be placed on improving the science-policy interface. This will likely require clear leadership from federal directives in addition to context-specific departmental initiatives.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".