Bibliographic record
Abstract
The conventional approach to performance appraisal and promotion has been widely criticized, often with good justification. Baron and Kreps (1999, p. 407), for example, note that promotion systems based on performance appraisals have ‘unhappy consequences’, while Coens and Jenkins (2000, p. 18) argue that appraisals should be abolished altogether because they are ‘ineffective and cause a spate of undesirable, unintended effects’. This paper reviews various problems associated with the conventional approach, and suggests that these problems stem from the faulty, questionable or outdated premises underlying the approach. It then outlines an alternative paradigm. This alternative paradigm has many attractive features: it is fair and objective; reduces intra-group conflict; identifies individuals with initiative and leadership skills; helps the organization to benefit from otherwise unrecognized opportunities; and is easy for managers to administer. It also helps to identify which departments are well managed. The paradigm will require a change in the organizational culture, which some organizations may have difficulty making.
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.
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 | 0.016 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".