Canada’s emerging foresight landscape: observations and lessons
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: to introduce scholars and practitioners of foresight to the emerging Canadian foresight ecosystem, and to provide lessons learned on developing policy foresight from the Government of Canada context. Design/methodology/approach The paper provides a series of lessons based in part on informal and indirect observations and engagement with established Canadian foresight entities, including Policy Horizons Canada, and numerous newly established foresight initiatives at Global Affairs Canada, Standards Council of Canada and the Canadian Forest Service. Findings The paper finds that Canada’s newly emerging foresight units and initiatives face structural, institutional and organizational challenges to their long-term success, including in concretely measuring foresight outcome (rather than simply output) in policy making. Originality/value The paper provides a unique and empirically driven perspective of the foresight ecosystem that has emerged within the Canadian federal public service since 2015. Lessons are culled from this emerging network of Canadian foresight practitioners for international application.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".