Guided Cultural Evolution and Sustainable Development: Proof of Concept and Exploratory Results
Bibliographic record
Abstract
This dissertation innovatively uses Cross-Impact Balances (CIB) to study how societal cultures evolve, and how societies might steer the evolution of their cultures towards sustainability. Societal cultures have been conceptualized as interdependent sets of worldviews, institutions, and technologies (WITs), which co-evolve and interact with their environment (Beddoe et al. 2009). CIB is a judgment-based, computational method for identifying internally consistent scenarios (self-reinforcing system states) and pathways (sequences of contradictory system states between pairs of self-reinforcing system states) (Weimer-Jehle 2006). CIB uses categorical variables to represent state-specific effects, allowing it to represent system change as an evolutionary process of recombination (e.g., of types of worldviews, institutions, and technologies). \n \nMy dissertation develops a new analytical approach for studying socio-cultural evolution; elaborates the WITs framework of socio-cultural evolution; and presents a dynamic model of socio-cultural evolution, along with validation and a set of exploratory results. Validation shows that the model performs well in reproducing the recent evolutionary histories of a sample of contemporary societies. The exploratory results provide preliminary answers to the following research questions: which combinations of worldviews, institutions, and technologies (societal cultures, or WITs) are self-reinforcing, and under what environmental selective pressures; which self-reinforcing societal cultures appear to be most compatible with sustainable development, and why; and how might an unsustainable, self-reinforcing societal culture be transformed into a sustainable, self-reinforcing societal culture? \n \nMy exploratory results suggest that societies have been converging towards a small number of highly contrasting cultural types, and that this polarization may be expected to continue. My exploratory results also suggest that achieving sustainable development may require a transformation in the international system from competition to cooperation. Achieving such a transformation might require societies to foster an inclusive social identity, in order to overcome potential sources of conflict that can stem from sharp cultural differences. \n \nMy dissertation breaks new ground in the study of guided cultural evolution and sustainability transformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".