Social polarization and behavioral intentions during the COVID-19 Pandemic
Bibliographic record
Abstract
Social polarization and behavioral intentions during the COVID-19 Pandemic (SPBICOVID) The SPBICOVID dataset addresses a critical research question: Are there modifiable factors associated with attitudes toward violent radicalization and COVID-19-related behavioral intentions? By including questions about experiences of adversity, beliefs in conspiracy theories, time spent online, support for violence, and levels of social connection, this dataset aims to inform multisectoral collaborations and guide program design to increase receptiveness to public health interventions, reduce social polarization, and better understand the interplay between psychosocial risk factors and maladaptive behaviors. Data Collection and Processing: Data was collected between January and March 2023 using a Qualtrics research panel service. The sample was designed to be representative of the Northeastern region of the United States. The survey was disseminated in English to participants (n=999) between the ages of 18-40 years old in New England (Maine, New Hampshire, Massachusetts, Connecticut, Rhode Island, and Vermont). The survey topics include demographic information, experiences with discrimination, bullying, acts of violence, COVID-19 exposure, perspectives on COVID-19 and vaccine conspiracy, social media use, social polarization, depression and anxiety, and support for violent radicalization and future orientation. Responses were anonymized and cleaned by Qualtrics to ensure all respondents answered the survey questions thoughtfully. The dataset was further cleaned by the research team to standardize qualitative responses for analyses.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".