Bibliographic record
Abstract
In 2016, the newly elected Liberal government introduced reforms to the appointments system following well established partisan selections by previous governments. The government claimed that the new policy would provide Canadians with a more honourable, merit-based appointment process that would be free from political interference. This paper assesses the extent to which the objectives of the new policy came to fruition. The research examines the process of citizen participation by evaluating 1,168 Governor-in-Council (GIC) appointments made by the Liberal government to 204 federal institutions over the first four years of the program. The paper compares this group to 1,428 GIC appointments made by the former Conservative government during its final term in office. The research explores whether the reforms have changed the type and quality of appointments to federal organizations since the new system came into effect. The analysis uses the publicly available demographic information of geographic location, educational background, occupation, and gender for each appointee. This paper provides critical insight into current and future processes of citizen participation and discusses its implications for democratic politics in Canada. The research shows that the Liberal reforms did not improve the quality of appointments to federal organizations. The relevant demographic information for the Liberal appointees was similar to that of the Harper government, which meant the new changes tended to focus on representation rather than qualifications.
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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".