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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".