The science behind “values”: Applying moral foundations theory to strategic foresight
Bibliographic record
Abstract
Abstract “Values” play an oversized role in strategic foresight: they help define scanning frameworks, direct scanning efforts, inform change driver and scenario development, and underpin change within various systems and domains (e.g., politics, society, etc.). And yet, values are largely understudied within foresight. They are rarely defined consistently or explored with reference to a theoretical model of how values emerge or evolve. Rather, values are researched using dissimilar methods depending on the foresight research at hand, which can lead to gaps in analysis and inconsistency between foresight projects. Moral Foundations Theory (MFT), a social psychological theory that identifies common human moral values, offers a solution. MFT describes six moral values or “foundations”—care, fairness, loyalty, authority, sanctity, and liberty—each explained through the evolutionary development of humanity and detectable across cultures. Within foresight, MFT can be applied to understand and identify shifts in the influence of different values, which can result in more novel and unexpected conclusions. With these potential benefits available, we propose adopting and adapting MFT for use within the foresight to improve the way it approaches, identifies, and utilizes values. Our article unpacks MFT into its core tenets and illustrates how it can be used to inform scanning, change driver development, and scenario construction.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".