Evaluation and Utilization of Policy Information in the Canadian Parliament: The Influence of External Policy Actors
Bibliographic record
Abstract
While policy information utilization in the Public Service has been the subject of investigation, little is known in Canada about how legislators seek out knowledge or respond to information provided by external actors. Often described as lacking influence within the policy process, the average Canadian MP is assumed to engage little in policy-making. Based on a survey conducted amongst Members of the Canadian Parliament in April 2013, this paper investigates how MPs engage with both internal and external sources of information and what are some of the potential factors that explain MPs’ utilization of policy knowledge. Findings indicate that internal sources of information are the most regularly consulted, yet that amongst external providers, academic research is valued most highly. In line with recent literature on policy networks, results suggest that personal contact between policy actors is one of the most important mechanisms to ensure a positive reception of information. The overall conclusion is that MPs continue to have a strong interest in policy and respond positively to lobbying, whether these are the efforts of industry associations or academics disseminating their research.
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 | 0.061 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".