Towards Cultural Sensitivity in Societal Development
Bibliographic record
Abstract
People across cultures differ in behaviours, thoughts and preferences. Cultural sensitivity – i.e., acknowledgment of these cultural differences – in development science is a postulate known since at least the 1960s, but has remained understudied. The goal of the current paper is to address this gap and to investigate folk theories of societal development, and in particular to identify both universal and culturally specific lay beliefs on what constitutes good societal development. In this study we collected data on preferences in social developmental from 2,684 participants across nine countries from five continents. We measured preferences towards twenty-eight different development aims, and separately for preferences towards three aims constituting Human Development Index. We used a comprehensive analysis approach, consisting of multidimensional scaling, analysis of variance, and pairwise comparisons to characterize universal and country specific preference patterns. Our results demonstrate that what people understand as modernization remains substantially universal across countries, but specific pathways of development and preferences towards these pathways tend to be different between countries. We also distinguished three facets of modernization: basics for modernization (e.g., trust, safety, economic development), welfare aims (e.g., poverty eradication, education), and inclusive aims (e.g., openness, gender equality, human rights). Importantly, in all studied countries, we found that each of the three types of modernization is much more preferred than conventional aims (e.g., military, demographic, religion). Cultural sensitivity may be reflected in how development is conceptualised and measured, and in this paper we propose a method of implementing our findings into development indexes
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".