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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".