Bibliographic record
Abstract
This article will explore evolving thoughts on how the social field can be an effective lens to address relational tensions within activist groups. Gobby (2020) defines relational tensions as the ideological and social tensions that emerge in an activist group due to power inequalities, which are significant internal barriers for these groups to achieve their goals. I will draw on social movement literature and Scharmer’s (2018) concept of social fields to show how the source conditions of the various individuals that make up these groups affect the quality of how they relate to each other, which give birth to practices and results that either align with their values or create conflictual tensions that can hold these groups back. Through a personal case study, I intend to show how, by shifting an activist group's social field towards one that places relationality at the forefront, these groups can improve how they work together and ultimately avoid breaking apart.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".