Canada’s emerging foresight landscape: observations and lessons
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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