An Index of the Quality of Life for European Countries: Evidence of Deprivation from EU-SILC Data
Why this work is in the frame
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Bibliographic record
Abstract
Starting from EU-SILC data, a sample survey that defines the harmonised lists of target primary (annual) and secondary (every four years or less frequently) variables transmitted to Eurostat by the 27 countries, we have chosen a set of about fifty indicators on a qualitative basis. An exploratory factorial analysis led us to accept only eleven variables distributed around three principle components, assuming that each of them could become, after further inquiry, an index of deprivation.\nThen we carried out the factorial analysis on the three principle components just found. The distribution of the eleven remaining data can be roughly interpreted as follows: the first group indicates material deprivation; the second one social deprivation; the third one can be labelled as depending on economic policy. Three factorial indexes consist in the factor score resulting from the factorial analysis on the partial indicators summarizing information supplied by each variable; the sum of our three indicators offers a global index of the quality of life (QL-index), whose values can be classified in order to identify groups of countries with similar conditions. A map will be drawn to overview the condition of the countries considered. We will test the three obtained indicators with the Spearman rho, comparing it with the ranking score of the Human Development Index and the Inequality Adjusted Human Development Index of European countries. The expected result is quite high correlation between them, mainly for the material deprivation index. The correlation with the ranking score will allow us to compare the relation of HDI, our QL-index, and the three components of it considered separately. The comparison between the two, the HD Index and the QL-Index, should reveal that the latter is more correlated with the IHDI. The greater number of indicators in our index should improve its explaining power, taking into account also social dimensions not so relevant in the HDI. The articulation of our index makes it possible to analyse the phenomenon more precisely; at the same time, the sum of the three indicators could be a good validation of the HDI.
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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 | 0.009 | 0.002 |
| 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 | 0.001 | 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 it