What matters most to the perception of community resilience in Romania?
Bibliographic record
Abstract
We aim to measure and explain the perception of community resilience in Romania. We use survey data from a country-representative sample of 1500 respondents. We rely on factor-based partial least squares path modeling to measure five reflective latent constructs from a CCRAM-type questionnaire. We use these constructs to extract a second-order formative latent construct representing an overall measure of community resilience. Next, we use three sub-dimensions of family resilience, along with individual resilience and several control variables to explain community resilience. Among the five sub-dimensions of the overall measure of community resilience, social trust exerts the highest contribution, followed by place attachment. The predictors of community resilience with the largest effect sizes are the three sub-dimensions of family resilience. The policies geared towards increasing community resilience might not be able to address the most important factors, at least in the case of Romania, because they pertain to informal group interaction, and lie outside the reach of formal administrative authority.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".