The integration of Strategic Foresight into government management: cases of Finland, Canada, Singapore, and Chile
Bibliographic record
Abstract
En un contexto global marcado por la incertidumbre, la complejidad y la aceleración de transformaciones sistémicas, los Estados enfrentan crecientes desafíos para anticipar e integrar respuestas estratégicas de largo plazo. Esta investigación analiza cómo Finlandia, Canadá, Singapur y Chile han incorporado la prospectiva estratégica en sus procesos de planificación estatal durante las últimas tres décadas, con el objetivo de identificar los factores que explican sus distintas formas de institucionalización. A su vez, propone una contribución teórica a los estudios de futuro, la prospectiva y la gobernanza anticipatoria desde un enfoque constructivista, post-positivista y la teoría de la complejidad. Mediante un diseño cualitativo comparado, sustentado en un estudio de caso múltiples y análisis configuracional crisp-set QCA, se identifican mecanismos institucionales, marcos normativos y condiciones habilitantes que facilitan u obstaculizan la integración de capacidades anticipatorias en el aparato estatal. Los hallazgos revelan configuraciones causales asociadas a mandatos vinculantes, legitimidad epistémica, participación deliberativa y estabilidad institucional; como factores clave para una institucionalización robusta. El caso chileno se examina como una experiencia en desarrollo, contrastada con modelos consolidados, aportando al debate sobre capacidades estatales anticipatorias y la construcción institucional del futuro.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".