Performance Appraisal and Raters' Errors Exploring Utilitarianism and Deontological Ethics in Evaluation
Bibliographic record
Abstract
Performance appraisals serve as crucial mechanisms for evaluating employee performance and informing decisions related to rewards, promotions, and developmental opportunities. However, the process is susceptible to various biases and errors, often stemming from the ethical frameworks guiding raters' judgments. The chapter explores the impact of utilitarianism and deontological ethics on rater's errors in performance appraisals. Utilitarianism, grounded in the principle of maximizing overall utility or outcomes, may lead raters to prioritize the consequences of performance evaluations over adherence to moral rules or principles. This can result in biases such as leniency or severity, where raters manipulate ratings to achieve desired outcomes or organizational goals. Conversely, deontological ethics, which emphasize adherence to moral duties and principles regardless of consequences, may lead to errors such as halo or horns effects, where raters allow a single characteristic to influence their overall evaluation of an employee.
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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 | 0.129 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".