Bibliographic record
Abstract
The term “psychic numbing” describes a paradoxical observation in human behaviour research: as the number of victims in a tragedy rises, people’s emotional response to that tragedy diminishes. Correspondingly, research has demonstrated the presence of the “singularity effect,” whereby our concern for a tragic event or social issue is increased when focus is narrowed onto fewer victims and more vivid victim information is made available. While these phenomena are supported by ample research, studies on the subject repeatedly overlook the impact that the cultural relevance of a particular tragic event or issue has on an individual’s degree of psychic numbing. To address this caveat, the present study proposes an online survey method to gather information from a diverse sample on individuals’ concern for the issue of police brutality (known to disproportionately affect Black Americans), based on both the level of victim information presented and participants’ ethnocultural identification with Black American cultures. Participants will be randomly assigned to either the one-victim (vivid victim information) or multiple-victims (low victim information) condition. We predict that for less (or non) ethno-culturally identifying individuals in particular, concern for police brutality, which is measured by willingness to donate and affective response, will be significantly higher in the one-victim condition versus the multiple-victims condition. Conversely, we predict that for strongly ethno- culturally identifying individuals, the psychic numbing effect will be diminished and concern will be similarly high regardless of the number of victims shown. This alludes to the importance of cultural relevance on an individual’s degree of psychic numbing.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".