Race, rhetoric, and participatory capture in U.S. housing: A critical discourse analysis of community building in HOPE VI
Bibliographic record
Abstract
Participatory capture refers to the manner in which institutions and organizations engaged in participatory projects deploy participatory methods, however unintended, in ways that further endanger or disempower the most vulnerable participants. To further articulate this phenomenon, this article presents a critical discourse analysis of key documents associated with HOPE VI, a federal public housing program in the United States known for its emphasis on community participation amid antiblack power structures. The result of the analysis are two models of participatory engagement—deficit and governance—that facilitated HOPE VI’s participatory capture. Deficit models rely on participants who are discursively rendered as “inferior,” while governance models prioritize “token” participants at the expense of more robust participant feedback. Moreover, these models, in the context of the U.S., carry with them particularly racialized predicates that demonstrate potential perils of participation for low-income, marginalized communities. With implications for organizational communication, diversity initiatives, and community engagement, the analysis presented in this article offers researchers, organizers, and policymakers ways to continually evaluate the efficacy of participatory methods, especially in racialized or hierarchical contexts.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.038 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".