Dangerously Close: The Reciprocal Link Between Social Closeness and Bad Behavior
Bibliographic record
Abstract
Reducing unethical behavior is an important goal for many organizations. Prior research demonstrates that social closeness – i.e., psychological or real social proximity to others – may reduce unethical behavior. Simply feeling close to others, using gestures to signal interpersonal closeness, belonging to a tight knit group, or being in a high density social network typically leads to less selfish and more other-focused, moral behavior. In the current symposium we explore the limitations of social closeness. This symposium engages a comprehensive methodological approach in order to understand when social closeness may have negative consequences, spanning levels of analysis from intrapersonal to social network. First, Lucas and Livingston explore how feeling socially connected increases people’s willingness to harm others in moral dilemmas (intrapersonal level). Next, Schroeder, Fishbach, Schein, and Gray demonstrate when intimate interactions lead to antisocial behavior (interpersonal level). Third, Effron and Knowles show how group entitativity licenses out- group prejudice (intragroup level). Finally, in a test of the reciprocal link, Lee, Im, and Parmar demonstrate how behaving unethically promotes the activation of a high-density network (social network level). By demonstrating the limitations of social closeness to promote ethical behavior, we identify potential areas for future theory building and research. Feeling Socially Connected Increases Utilitarian Choices in Moral Dilemmas Presenter: Brian Lucas; Northwestern Kellogg School of Management Presenter: Robert W. Livingston; U. of Sussex Thick as Thieves? Consequences of Dishonest Behavior on Egocentric Social Network Presenter: Jooa Julia Lee; Harvard U. Presenter: Dong-Kyun Im; U. of Seoul Presenter: Bidhan Parmar; U. of Virginia People Behave Antisocially in Intimate Instrumental Interactions Presenter: Juliana Schroeder; The U. of Chicago Presenter: Ayelet Fishbach; The U. of Chicago Presenter: Chelsea Schein; U. of North Carolina, Chapel Hill Presenter: Kurt Gray; U. of North Carolina, Chapel Hill How Belonging to a Cohesive Group Allows People to Express Their Prejudices Presenter: Daniel A. Effron; London Business School Presenter: Eric David Knowles; U. of California, Irvine
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".