Bibliographic record
Abstract
The rise of conservative populism in the United States is evident with the election of Donald Trump whose campaign focused on a fear of otherness, a resentment of elites (including bureaucrats), and an emphasis on self-interest. This paper will focus on populist policies and actions introduced during Stephen Harper’s tenure in government. Many of these policies were directed specifically towards the public service including the reduction of public servants, unilateral changes to labour laws, and the politicization of the public service through a proposed niqab ban. While the influence of populist conservative policies has caused noticeable resentments between public servants and the Conservative Party, public servants should reconsider their position within democracy as not focused on their compatibility with the government of the day, but rather with their adherence to the values of inclusion, fairness, and neutrality. While the public service must constantly adapt to the will of the political branch of government including more populist governments, these core values should be celebrated through active implementation and leadership to aid in the development of a positive relationship with both the political arm of government and the public more broadly. These values provide a means for individual public servants to invest their motivation into values that they can treat as fundamental to their role in democracy.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.044 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".