Bibliographic record
Abstract
Purpose – This exploratory article aims to define foresight and consider its use in public management. Impediments to foresight best practices are also discussed. Design/methodology/approach – The research is based on the literature, including both primary and secondary sources. Canada serves as a case study to discuss foresight practices. Findings – Foresight has the potential to be useful from a governance perspective. Foresight practices, however, are limited by the need to overcome departmental boundaries, political impediments and, arguably, governments' abating policy capacity. Research limitations/implications – The purpose of this article is to introduce foresight limiting to an extent the depth of the analysis. Canada is neither a foresight leader nor at the bottom of the list. Conclusions drawn from this case are, despite differing political and administrative contexts, representative of problems faced by many governments in using foresight. Originality/value – The field of public management has paid little attention to foresight, though governments do make use of this instrument. This article is one of the first to consider foresight, not from the perspective of the futures field, but from that of the discipline of public management.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".