Innocent Expertise: Subjectivity and Opportunities for Subversion within Community Practice
Bibliographic record
Abstract
This article shines light on the tension-filled subjectivity of community practice, reflecting on the experiences of two community workers and our stories of how our whiteness functions in community practice—particularly when white workers engage with racialized community members. Through these stories, we seek to demonstrate how the white worker is framed as what we call an “innocent expert subject”; this subject is one that is both constituted through the authority of the systems we work within, and one which we construct through our own actions. Potential dangers of this subjectivity are then explored, as are opportunities for subversion. As we work toward negating the potential harms caused by this fraught subjectivity, it is our hope that possibilities for different kinds of community work, and different kinds of white community workers, can be realized.
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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 | 0.034 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.024 | 0.130 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".