Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is growing interest in the United States and elsewhere in the use of a points-based system for selecting immigrants on the basis of their observed human capital. This paper explores the design of an optimal skills-based immigrant selection system based on two basic elements: a predicted-earnings threshold for determining whom to accept and reject, and a human-capital-based earnings regression for making error-minimizing predictions of immigrant success in the host labor market. We first show how to design a points system based on what are assumed to be the optimal predicted-earnings threshold and the optimal prediction regression. We next develop a method for identifying the optimal threshold given the prediction regression. The method produces a ¿selection frontier¿ that describes the options facing policy makers. The frontier shows the tradeoff between the average quality of admitted immigrants and the number of immigrants admitted. The frontier shifts out with improved accuracy in predicting earnings as well as with increases in the variation and average quality of the applicant pool. Finally, we show how the policy maker chooses the optimal selection system given the selection frontier.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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