Generational Divide: A New Model to Measure and Prevent Youth Social and Economic Discrimination
Bibliographic record
Abstract
Measures concerning intergenerational inequality generally refer to youth unemployment or youth household income and wealth. The common conclusion is that the generational gap is represented by negative trend of youth unemployment and NEETS. In this paper, I argue that this phenomenon is not the cause of intergenerational unfairness, but one of its effects.The pioneering efforts to measure the intergenerational fairness through a set of multidimensional indicators are the starting point for deeper analysis. The purpose of this paper, however, is not only to measure unfairness, but to quantify the generational divide. The latter is defined as the intensity of material and immaterial barriers affecting a sound development of individuals.The way to measure such a phenomenon is to use a new and comprehensive, synthetic index, applied to the Italian youth emergency and social discrimination. For this reason some new indicators are added to the previous models, such as credit crunch, the scar inferred to NEET (Not in Education, Employment or Training), digital divide and barriers to mobility.The results of this pilot analysis in the selected country for the period 2004-2012 demonstrate a worsening of the generational divide over the last five years and these high negative trends are mainly due to housing costs, decreasing incomes and the pension burden. These results suggest the need for a deep and careful consideration on the real intergenerational sustainability of current European and development strategies and show a large discrimination towards the younger generations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".