Can performance-feedback accuracy be improved? Effects of rater priming and rating-scale format on rating accuracy.
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
Performance appraisal information is often used for employee feedback and development. Research has found that assessments that are global (i.e., based on broad aspects of performance) and comparative (i.e., explicit interratee comparisons) may be most accurate in terms of Cronbach's (1955) differential accuracy, a type of accuracy that is directly relevant to the provision of feedback. Unfortunately, a global-comparative assessment may not give recipients the most useful diagnostic feedback. In this experiment, an innovative rater-priming manipulation was developed and tested on a sample of 109 participants. The priming manipulation had the effect of improving differential accuracy and providing diagnostic feedback. A 2nd independent variable involving 2 different Behavioral Observation Scale formats also was investigated. Explanations of findings, limitations of this experiment, directions for future research, and implications for performance appraisal practice are discussed.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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