Evaluating advisors: A policy‐capturing study under conditions of complete and missing information
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
Abstract Decision‐makers' relative preferences for various advisor characteristics were investigated in two multilevel policy‐capturing studies. The characteristics under consideration were: advisor expertise, advisor confidence, advisor intentions, and whether that advisor was the sole available source of advice. In Study 1, decision‐makers had access to all relevant information about the advisors. In contrast, some relevant information about the advisors was systematically made unavailable in Study 2, which allowed an investigation of the effect of missing information on decision‐makers' evaluations of advisors. Results from both studies indicated that advisor expertise and intentions were most important in promoting decision‐makers' positive evaluations of advisors, that this effect was even more pronounced under conditions of missing information, and that advisor expertise and intentions also interacted synergistically. Given that expertise and good intentions are determinants of an advisor's trustworthiness, the results highlight the interpersonal nature of advice giving and taking. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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