Encountering: reimagining organised encounters through radical relationality
Bibliographic record
Abstract
Sociological and social science researchers increasingly seek to explore the potential of facilitating organised encounters between conflicting social groups, hoping that such meetings might promote positive social change. Today, a large body of practice relies on these orchestrated interactions to try to reduce conflict across social, religious, and cultural differences. However, we argue that this growing literature tends to assume bounded conceptions of groups, narrow views of power, and linear ideas of temporality. Drawing on emerging developments in relational sociological theory, we foreground using the verb encountering (as a dynamic process of relating) rather than encounter (as a discrete event) as an alternative framework for researchers as they facilitate, manage, and interpret these orchestrated meetings. Advancing radical relationism in this way, we argue, sheds new light on the multifaceted, emergent dynamics of such meetings, enabling a more complex and deeper understanding of how they work. Thus, radical relationism, via the idea of encountering, provides an alternative framework for conducting sociological research on what has come to be known as organised encounters.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".