Bibliographic record
Abstract
Purpose This study seeks to explore the incidence and severity of inconsistency in the application of disciplinary measures between supervisors, given the same disciplinary incident. Consistency is an important aspect of procedural fairness in disciplinary action, but it has received little empirical attention. Design/methodology/approach Four employee discipline scenarios were assigned at random to 130 real‐life supervisor‐employee dyads, who role‐played the scenario. Findings There was little consistency between supervisors in their decisions regarding disciplinary measures. Overall, having an informal discussion with the employee was the most common response. Only when specific instructions to impose a verbal or written warning were provided did most supervisors move beyond an informal discussion. Even when clear instructions were given, a substantial minority applied a less severe disciplinary outcome. Research limitations/implications Even in this role‐play situation, where “real life” variables such as union grievances that could lead to the dilution of disciplinary action were not present, supervisors were generally lenient regarding employee discipline. Practical implications The trade‐off between the objectives of consistency and consideration of individual circumstances presents a serious challenge to practising supervisors. Originality/value This is a rare empirical paper exploring the issue of consistency in employee discipline.
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 | 0.090 | 0.317 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".