Who are you more likely to help? Relationship status and empathy predict helping
Bibliographic record
Abstract
Problem/Purpose: Bystander effect refers to the idea that people are less likely to help someone in need when there are other people present. Instead a negative relationship is present, in that a person’s feeling of responsibility to help decreases as the number of other bystanders increases. The current study is examining factors that might better predict engaging in helping behaviors. Past research has found that empathy plays a role in situations, and that as people relate to others’ experiences, they feel an increased need to help and engage in helping behaviors (Paciello et al., 2013). Sierksma and colleagues (2014) examined helping behavior in children by giving them vignettes and asking how likely they would help in those situations. They found that the children were more likely to help in the situations where a friend was present, compared to if there was a stranger or no one present. Thus, the question remains whether empathy is only influential depending upon the relationship to the victim. That is, would individuals be more likely to intervene if the helping behavior affected a friend compared to a stranger. The current study examined the influence of empathy, in conjunction with the relationship status to the victim, to determine likelihood of helping. Procedure: This study is a 2(victim status: friend, stranger) X 2(effort: low, high) double-blind mixed method experiment. Specifically, participants are randomly assigned to read nine vignettes that vary on relationship to victim, report their likelihood to help, and level of effort (i.e., low effort, high effort) they are willing to exert while helping. After completing the vignettes, participants will complete the Toronto Empathy Questionnaire (TEQ; Spreng, McKinnon, Mar, & Levine, 2009). Expected Results: Data collection is still underway, but it is expected that helping behavior will be higher for friends than for strangers on general helping. It is also expected that empathy will be positively correlated with general helping behavior. Conclusion and Implications: When someone is needing help, it isn’t always obvious and our first instinct to do. It can be hard to know how to help. This study examines varying ways the participants could help in each scenario and determines which factor (e.g., relationship status, empathy) may play a role in their helping behavior. By understanding those factors, psychologists could provide education and guidelines that help increase engagement in pro-social behavior. Perhaps, we can teach individuals how to become a more proactive bystander.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".