Bibliographic record
Abstract
“Necessary evils” require employees to psychologically or physically harm others to produce a perceived greater good. Employees can also help others during necessary evils tasks by providing assistance and support to those harmed. Through an inductive, qualitative study of human resources employees’ experiences carrying out downsizing, we explore how the perception of helping the person one has harmed relates to the harm-doer’s ability to withstand the challenges of having to carry out necessary evils. Our research culminates in a theoretical model showing that (a) seven kinds of stressors were associated with participants’ involvement in necessary evils tasks, (b) these stressors triggered a series of negative personal outcomes (negative self-focused emotions, emotional exhaustion, and intention to turnover), and (c) perceived prosocial impacts ameliorated these negative personal outcomes. We discuss the implications our findings for research and practice, address limitations of our study, and offer ideas for future research.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".