Moralization in the Workplace: Implications for Motivation, Inclusion, and Misconduct
Bibliographic record
Abstract
Moralization is a crucial and growing topic in psychology and related disciplines such as political science , anthropology neuroscience , and sociology. Yet, research on the implications of moralization for organizations remains sparse. Indeed, although the literature on the implications of moralization for organizations is growing, significant questions remain. For instance, can efficiency—the ratio of process output to resource use—be moralized by managers and what might its consequences be for managerial practice? And can we capture the moralization of organizational actions by members of the public through their discourse and can we use this to predict their willingness to buy products from the organization? This symposium brings together scholars who answer such questions by placing moralization within the organizational context and addressing its implications for workplace efficiency, motivation, inclusion, and misconduct. Taken together, the four talks that are part of this symposium highlight where research on moralization within organizations is going and the practical implications it provides for managerial practice.
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 | 0.048 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.008 | 0.013 |
| 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".