Can We Improve Choice Model Parameter Estimates by Jointly Modelling SP Choices with Corresponding Elicited Confidence Ratings
Bibliographic record
Abstract
The paper proposes a closed-form joint econometric model of Stated Preference (SP) choices and corresponding confidence rating elicitation through the measure of choice model entropy functions. The proposed econometric model is estimated empirically by using two SP survey datasets collected in Vancouver and Toronto. Empirical models reveal that the SP choice task’s complexity directly influences SP confidence ratings, and there is an endogenous relationship between them. It is clear that capturing such endogeneity does improve the parameter estimates of choice model components. The most important improvements are the increases in statistical significances of choice model parameters in the joint model, some of which may be even insignificant in the univariate choice model. Moreover, evidence ignoring such endogeneity may cause under-estimation of the marginal rate of substitutions of the SP choice attributes. However, the level and extant of such benefit gains vary by the nature and types of SP experiments.
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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 | 0.022 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".