Redesigning performance appraisals for improved management
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
Summary form only given. Rating employees is a stressful experience for most supervisors; the stress primarily arises with mid-range performers. These performers, who are not "stars" and never likely to be so, make a positive contribution to a company but are reminded once a year of their ordinariness, a message that is unpleasant to give and receive. Supervisors have developed a number of strategies to avoid giving this message. Companies have a legitimate need to rate and rank employees, in part because identifying high performers and making sure these get the message that their performance is recognized and will continue to be rewarded is a key to retention, especially for knowledge workers. However, the need to rate employees does not equate to the need to tell average performers that they are average on an annual basis. Companies also need to provide goal setting and coaching to employees regardless of performance level. Goal setting ensures that the employee's objectives reflect the company's shifting objectives, and coaching enhances performance for virtually all employees. These functions do not need to take place at the same time as rating, and for the average employee the focus on the ego-damaging message often ensures that such constructive comments get little attention. An HR department can play a key role in helping managers distinguish between rating and coaching, and in helping emphasize that different employees need different messages and different treatment from the company, depending on their performance level. One emphasis of this approach is a focus on fostering a sense of esteem for good average performers.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it