Bibliographic record
Abstract
Can science fictioning, co-creation, and other innovative, design-centered foresight techniques find fertile ground within the marble halls of government? Find out in this critical examination of strategies, methods and lessons from the project, Economic Futures for Ontario 2032 (EFO). Led by Strategic Innovation Lab (sLab) at OCAD University, in close collaboration with an interdisciplinary governmental working group, EFO explores challenging futures for Canada’s most populous and diverse province. The project attracted hundreds of participants from public and private sectors. \n \nBalancing creativity and surprise with evidence and policy relevance, EFO is a demonstration initiative designed to boost organizational learning through scanning, scenarios, and strategic implications. The joint sLab/government team co-authored together, producing unexpected ideas and exceptional stakeholder ownership. As governments cope with shifting public sentiment, dwindling coffers, and rising complexity, need has never been greater for innovative anticipatory planning. In this session we’ll interrogate a path with real risks and rewards.
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.
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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".