Seeking Purity, Avoiding Pollution: Strategies for Moral Career Building
Bibliographic record
Abstract
This study builds theory on how people construct moral careers. Analyzing interviews with 102 journalists, we show how people build moral careers by seeking jobs that allow them to fulfill both the institution’s moral obligations and their own material aims. We theorize a process model that traces three common moral claiming strategies that people use over time: conventional, supplemental, and reoriented. Using these strategies, people accept or alter purity and pollution rules, identify appropriate jobs, and orient themselves to specific audiences for validation of their moral claims. People’s careers are punctuated by reckonings that cause them to reconsider how their strategies fulfill their moral and material aims. Experiences of gender and racial discrimination, access to alternate occupational identities, and timing of entry into the occupation also shape people’s movement between strategies. Over time, people combine these moral claiming strategies in different ways such that varying moral careers emerge within the same occupation. Overall, our study shows how people can build moral careers by actively revising purity and pollution rules while holding fast to institutional moral obligations. By theorizing careers as an ongoing series of moral claiming strategies, this research contributes novel ideas about how morals weave through and organize relationships between people, careers, and institutions.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| 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".