Psychologizing indexes of societal progress: Accounting for cultural diversity in preferred developmental pathways
Bibliographic record
Abstract
Since the Second World War, the dominating paradigm of societal development has focused on economic growth. While economic growth has improved the quality of human life in a variety of ways, we posit that the identification of economic growth as the primary societal goal is culture-blind because preferences for developmental pathways likely vary between societies. We argue that the cultural diversity of developmental goals and the pathways leading to these goals could be reflected in a culturally sensitive approach to assessing societal development. For the vast majority of post-materialistic societies, it is an urgent necessity to prepare culturally sensitive compasses on how to develop next, and to start conceptualizing growth in a more nuanced and culturally responsive way. Furthermore, we propose that cultural sensitivity in measuring societal growth could also be applied to existing development indicators (e.g. the Human Development Index). We call for cultural researchers, in cooperation with development economists and other social scientists, to prepare a new cultural map of developmental goals, and to create and adapt development indexes that are more culturally sensitive. This innovation could ultimately help social planners understand the diverse pathways of development and assess the degree to which societies are progressing in a self-determined and indigenously valued manner.
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".