Tackling the Geopolitics of Standardization: Lessons from Canada's Strategic Foresight-to-Standards Pilot Project
Bibliographic record
Abstract
Strategic foresight is the systematic exploration of emerging and future developments. Standardization is the process by which a common technical language is created and applied to new concepts and evolving technologies. Both strategic foresight and standardization address long-term technological change across industries, societies, and economies. And yet rarely are the two used in tandem to anticipate emerging standardization priorities that are critical to national interests. Against the backdrop of a global “technological race,” the foresight-to-standards process provides a novel approach to anticipate the nature of emerging technologies and their plausible influence on national, military, and economic interests, and to direct standardization efforts to align with strategic objectives. Our article provides an in-depth exploration of the Standards Council of Canada's experimentation with foresight between 2018 and 2021, informed by first-hand experience and observation. We describe and assess the SCC's use of strategic foresight in standardization, providing insights on capacity building, collaboration, leadership, decision making, and action.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.030 | 0.017 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".