Does a Small Country Have Meaningful Regional Personality Differences? The Case of Estonia
Bibliographic record
Abstract
BACKGROUND: Regional differences in the Big Five personality domains have been observed in several countries at different geographical granularities, often correlating with regional political, economic, social, and health (PESH) indicators. OBJECTIVE: We examined the extent of regional personality differences in Estonia and whether these differences were meaningfully correlated with PESH indicators. METHODS: Using data from the Estonian Biobank (N = 72,268; 7% of the adult population, providing unprecedented representativeness), we tested regional personality differences and their relations with PESH indicators with and without spatial smoothing. RESULTS: We found that regional Big Five scores varied by 1.19 (extraversion) to 2.78 (openness) T-score units across counties (N = 15) and by 2.80 (extraversion) to 4.74 (openness) units across municipalities (n = 74). Also, the correlations with the PESH indicators at the county and municipality levels persisted even after controlling for gender, age, and spatial dependency, and were moderately consistent with our predictions (r = 0.23 to 0.30) and between the county and municipality levels (r = 0.41). CONCLUSIONS: Estonian residents tended to be similar in personality traits regardless of their location, replicating results from other countries. Yet, small regional personality domain differences could represent valid and possibly consequential psychological variation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".