Protecting White Interests: A Case Example of Interest Convergence in Policymaking
Bibliographic record
Abstract
Although various policy analysis frameworks exist within the social work literature, fewspecifically address the racism inherent to policymaking processes. We propose interestconvergence as a conceptual lens for policy analysis to expose the racism inherent inpolicymaking. Transcripts from 19 public hearings of five bills sponsored during the 2017Nevada legislative session were included in the data analysis for this study. A thematic analysistook place at the latent level to identify underlying concepts, assumptions, and ideas within thedata (Braun & Clarke, 2006). Results indicate that the public testimony process and ultimateoutcomes of public policy making protect white interests which sustains structural racism. Inunderstanding the dynamics of interest convergence in policymaking, social work educators,policy advocates, and macro-practitioners would be better equipped to impact the policymakingprocesses focused on racial equity.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".