Call for Papers: Special issue on “Community psychology in the era of <scp>COVID</scp>‐19: How the pandemic has influenced communities and communities’ reactions”
Bibliographic record
Abstract
The Journal of Community and Applied Social Psychology is inviting papers for a special issue on “Community psychology in the era of COVID-19: How the pandemic has influenced communities and communities” reactions’. The pandemic is by no doubt an exceptional event, a challenge on a global scale with which modern societies have been forced to cope. It is common experience that this emergency has deeply affected human communities at all levels. Although there is still uncertainty about the future of the pandemic, communities need to react immediately in order to prevent the diffusion of the virus. This implies a deep modification in community lifestyle all around the world, that is going to change the very nature of social relations. In such panorama, studying the impact of Covid-19 at the interpersonal, social and intergroup levels is of paramount importance. In this regard, social and community psychology can play a central role, by improving our understanding of the social psychological processes that characterize communities’ reactions to the pandemic. Since community responses varied greatly across world countries, we are interested in the impact of the pandemic in the different countries all around the world. The special issue focuses on the COVID-19 pandemic, how it is affecting communities at all levels, how communities are reacting or might react, also considering how the pandemic is dynamically evolving all around the world. We welcome papers addressing broad topics in the context of COVID-19 related to communities. In particular, we welcome empirical articles, but also review articles related to this emergency (also presenting theoretical models of how communities can deal with/react to the pandemic), commentaries, and praxis reports. Interdisciplinary papers that extend beyond social, political and community psychology are also encouraged under this call. Authors are invited to read the Author Guidelines for submission guidance.
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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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.002 | 0.043 |
| 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; both teacher heads agree on what is shown here.
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".