The futures of Canadian governance: Foresight competencies for public administration in the digital era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it