“Mainstreaming” foresight program development in the public sector
Bibliographic record
Abstract
Purpose This paper aims to develop a framework for benchmarking the maturity of public sector foresight programs and outlines strategies that program managers can use to overcome obstacles to foresight program development in government. Design/methodology/approach The public sector foresight benchmarking framework is informed by a bibliometric analysis and comprehensive review of the literature on public sector foresight, as well as three rounds of semi-structured interviews conducted over the course of a collaborative 18-month project with a relatively young department-level foresight program at the government of an Organisation for Economic Co-operation and Development (OECD) country. The paper frames public sector organizations as “complex adaptive systems” and draws from other government initiatives that require fundamental organizational change, namely, “gender mainstreaming”. Findings Nascent or less mature programs tend to be output-focused and disconnected from the policy cycle, while more mature programs balance outputs and participation as they intervene strategically in the policy cycle. Foresight program development requires that managers simultaneously pursue change at three levels: technical, structural and cultural. Therefore, successful strategies are multi-dimensional, incremental and iterative. Originality/value The paper addresses two important gaps in the literature on public sector foresight programs by comprehensively describing the key attributes of mature and immature public sector foresight programs, and providing flexible, practical strategies for program development. The paper also pushes the boundaries of thinking about foresight by integrating insights from complexity theory and complexity-informed organizational change theory.
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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 | 0.061 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".