The Study of Returns to Private Investment in Higher Education from the Point of Employment LES ETUDES SUR LES RENDEMENTS DE L'INVESTISSEMENT PRIVE DANS L'ENSEIGNEMENT SUPERIEUR DU POINT DE VUE DE L'EMPLOI
Bibliographic record
Abstract
The quantitative methods are used to compare the difference of vocation and employment between the university graduates and high school graduates, which including the secondary school graduates. And the following four respects are involved to describe the impact of higher education to employment: the relative concentration degree, the difference of vocation, the weekly working time and the working industry. So we come to the conclution from the aspect of employment that private investment gets not only great market returns but also nonmarket returns. Key words: higher education; private investment; non-market returns; employment Resume: Les methodes quantitatives sont utilisees pour comparer les differences dans la vacation et dans le type de travail entre les diplomes des universites et les diplomes des colleges, y compris les ecoles secondaires. Les quatre aspects suivants sont impliques pour decrire l’impact d’une education superieure sur l’emploi : le degre de concentration relatif, les differences de vocation, les heures de travail hebdomadaires, et le type de l’industrie dans lequelle ils travaillent. Du point de vue de l’emploi, nous arrivons a la conculsion que l’investissment prive peut obtenir non seulement des rendements du marche mais aussi des rendements qui ne proviennent pas du marche. Mots-Cles: enseignement superieur; investissement superieur; rendements qui ne proviennent pas du marche; emploi
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".