Caucusing Updated: Innovations to Build Belonging and Empowerment
Bibliographic record
Abstract
Caucusing, as a social justice activity, is traditionally implemented to provide an insider space for marginalized persons to share experiences and build a space for belonging and safety by excluding those who hold privileged identities. Within a particular event that combines privileged and oppressed, experiences are uneven, with insiders experiencing inclusion, while outsiders have a largely isolating experience, although intended to be a place to interrogate privilege. It is not an activity to build community across identities, nor is it the intention. Through expansive reflection on a course activity, the authors share their experience of an updated caucusing activity, a three-part undertaking that first holds caucuses where all students participate, subsequently holds cross-identity dialogues, and then dialogues are repeated with different groupings. Informing this article are the authors’ separate and dialogic reflections, activity evaluations and follow-up comments by students. Results reveal high potential for building belonging and community within and across identities, potentially relevant to numerous service professionals. Caution exists when groups have low levels of engagement with each other and trusting experiences with each other and the instructor. A less successful version of this activity occurred in a part-time program which suggests this caution is warranted.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".