Individual Human Capitals, Collective Behavior, and Age-Earnings Profiles of Workers: an Alternative Rationale and New Path to Modeling.
Bibliographic record
Abstract
In the article, an improved macro-level model is presented. The improvement concerns statistic averaging -- more reasonable distribution (Weibull) of individual "social" ages at intertemporal decision-making is used to get the macro-model. Simple (linear) regression based on the macro-model's specification is used to fit large samples (more than 40 age points) of empirical age-earnings data (UK, 2002; Canada, 1973, 1984, 1994). The found estimates of the pre-supposed "social" ages of persons to switch back and forth their educational efforts during life span are quite reasonable as well as the gender differences "detected" by the macro-model in such behavior. Findings presented in formulae, tables and graphs demonstrate the developed model's high accuracy in the regressions - better than in the Minceranian log-linear regressions.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".