“Upload Your Impact”: Can Digital Enclaves Enable Participation in Racialized Markets?
Bibliographic record
Abstract
Ethnoracial minorities are often racialized and consequently excluded from various consumption contexts. Racialized market actors strive to overcome exclusion and gain participation in markets; however, these efforts are often insufficient because they cannot create equitable access to market resources, fair opportunities for voice, and empowerment to shape market practices. This research identifies digital enclave movements as a unique means by which racialized market actors redirect their resources and mobilize digital network tools to participate in markets. Using a qualitative study of the digital enclave #MyBlackReceipt, the authors explore tactics supporting the formation and sustenance of digital enclaves and how they support participation in markets. The authors identify five tactics that racialized market actors employ to foster digital enclaves and enhance market participation: legitimizing, delimiting, vitalizing, manifesting, and bridging. Last, the authors provide recommendations for policy makers on how to support and foster more equitable participation of ethnic minority groups in markets while addressing the risks of radicalization and the backlash related to enclaves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".