The futures of Canadian governance: Foresight competencies for public administration in the digital era
Bibliographic record
Abstract
Abstract Evidence‐based practice has advanced in public administration, with increasing reliance on social research and population sampling in decision making. Yet the evidence‐based turn risks marginalizing the value of strategic foresight and futures competencies in informing policy and planning. Where evidence enables policymakers to select the best near‐term course of action, future outcomes are inferred and projected, and not determined by past evidence. Foresight provides a necessary competency for defining and investing in the right direction of future policy and action, by articulating future problematics with multiple foresight methods. While social and technological futures cannot be precisely predicted, future scenarios and prospectuses can be designed to inform options and trajectories for intervention and new policy. The emerging area of digital‐era governance is examined, where complex scenarios for future policies are based on present evidence (such as trends) and informed speculation to formulate policies and options in dynamically changing societal contexts.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".