Harm, Then Good? How Work Meaningfulness Emerges from Doing Harm
Bibliographic record
Abstract
Abstract Meaningful work has been identified as an important antecedent of an array of positive outcomes for both workers and their employers. However, many work roles involve harming others, an experience that has previously been linked to negative outcomes such as dissatisfaction and burnout. How does meaningfulness emerge when one's work includes such challenging circumstances? Drawing on interviews and observations in the veterinary industry, we elucidate new theory about the relationship between harm‐doing and the experience of meaningful work. Workers' interpretations of the worthiness of the harm, as well as the types of actions they take to remediate it, influence whether their involvement in harm‐doing episodes undermines or heightens their sense of meaningfulness. We further detail how dimensions of harm‐doing episodes shape opportunities for remediation, as well as whether the episodes ‘stick’ in workers' memories and hence figure into their ongoing, holistic accounts of work meaningfulness. Based on these findings, we introduce a novel ‘work‐bounded, worker‐centric’ view of meaningfulness that incorporates both the nature of work and workers' interpretations of it. Our research has implications for the work meaningfulness and workplace harm literatures, as well as for the many individuals whose work involves doing harm.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".