The Trudeau government and GIC appointments in Canada
Bibliographic record
Abstract
Purpose According to the traditional view of public administration, a critical component of good policy formulation is the provision of frank and fearless advice to elected decision-makers. This advice can be provided by permanent public officials or by the people selected by the elected governments to fill key and continuing posts. However, there are major questions as to whether new Governor-in-Council (GIC) appointment processes rooted in new public governance (NPG) are yielding the expected results promised, such as less partisanism, as a consideration for appointment. Design/methodology/approach The paper uses a mixed methods approach to examine the GIC process as it is used in Canada. In using these methods, the authors employed interviews with senior officials, governmental documents review and expert validation interviews to triangulate its main findings. Findings The paper uses the case of the revised appointment process for GIC appointments in Canada and suggests that the new arrangements do not deliver on merit-based criteria that ensures independence is protected between political executive and senior bureaucratic officials. Although new processes may be more open and transparent than past processes, the paper suggests that such processes are more susceptible to partisan influence under the guise of being merit-based. Research limitations/implications The research was limited to one country context, Canada. As such, it will be necessary to expand this to other Westminster countries. Testing whether manifestations of new public governance in appointment processes elsewhere will be important to validate whether Canada is unique or not. Practical implications The authors are left to wonder if this innovation of merit-based appointments in the new administrative state is obscuring the lines of accountability and whether it forms the basis for good policy advice despite promises to the contrary. Social implications Trust in the government is affected by decisions behind closed doors. They appear partisan, even when they may not be. Process matters if only to highlight increased value placed on meritorious appointments. Originality/value Previous studies on GIC appointments have generally been to explore representation as a value. That is, studies have questioned whether diversity is maintained, for example. However, few studies have explored appointment processes using institutional approaches to examine whether reforms to such processes have respected key principles, such as merit and accountability.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".