The Ethical Perils of Personal, Communal Relations: A Language Perspective
Bibliographic record
Abstract
Most companies use codes of conduct, ethics training, and regular communication to ensure that employees know about rules to follow to avoid misconduct. In the present research, we focused on the type of language used in codes of conduct and showed that impersonal language (e.g., “employees” or “members”) and personal, communal language (e.g., “we”) lead to different behaviors because they change how people perceive the group or organization of which they are a part. Using multiple methods, including lab- and field-based experiments (total N = 1,443), and a large data set of S&P 500 firms (i.e., publicly traded, large U.S. companies that are part of the S&P 500 stock market index), we robustly demonstrated that personal, communal language (compared with impersonal language) influences perceptions of a group’s warmth, which, in turn, increases levels of dishonesty among its members.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".