Organizing Transgender People: Toward a Process-Based Theory of Representative Bureaucracy
Bibliographic record
Abstract
Despite advancements in LGBTQ+ rights, transgender people remain as one of the most socially stigmatized and marginalized members of society. Many continue to face state-sanctioned discrimination. Kerala stands out in supporting and advancing transgender people. We interviewed 15 government officials and 28 transgender women to explore how the Kerala government uplifted and improved the lives of its transgender people. Using a grounded theory approach, we analyze Kerala’s (one of the most progressive states in India) efforts to support transgender people. We found that access to common good—linked with citizenship, active participation, and collective action in society—is key to connect policies with outcomes for socially marginalized groups. Our study emphasizes how representative bureaucracy can empower society’s most vulnerable individuals and help them establish nonprofit organizations for their own support. This discovery allowed us to enhance and develop a process-based theory of representative bureaucracy. Our study extends the theory by establishing the mechanisms by which representative bureaucracy delivers the common good to society’s marginalized members.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".