Stated preferences over job characteristics: A panel study
Bibliographic record
Abstract
Abstract When making choices over jobs with different characteristics, what trade‐offs are decision‐makers willing to make? Such a question is difficult to address using typical household surveys that provide a limited amount of information on the attributes of the jobs. To address this question, a small but growing number of studies have turned to the use of stated preference experiments; but the extent to which stated choices by respondents reflect systematic trade‐offs across job characteristics remains an open question. We use two popular types of experiments (profile case best–worst scaling and multi‐profile case best–worst scaling) to elicit job preferences of nursing students and junior nurses in Australia. Each person participated in both types of experiments twice, within a span of about 15 months. Using a novel joint likelihood approach that links a decision‐maker's preferences across the two types of experiments and over time, we find that the decision‐makers make similar trade‐offs across job characteristics in both types of experiments and in both time periods, except for the trade‐off between salary and other attributes. The valuation of salary falls significantly over time relative to other job attributes for both types of experiments. Also, within each period, salary is less valued in the profile case compared to the more traditional multi‐profile case.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".