Mapping institutional mechanisms of ethno‐national representation: towards a better measurement approach
Bibliographic record
Abstract
Abstract Measuring institutional mechanisms that facilitate ethno‐national representation is a difficult enterprise. Most studies examine the electoral system, while a set of other indices focus on designs and policies related to minority recognition. This article addresses a number of gaps in the existing literature by taking a wide view that considers a breadth of institutional designs that facilitate representation in a political system. The goal is to recalibrate our theoretical and empirical approach to measuring ethno‐national representation – to move beyond narrower assessments based solely on the electoral system, while also providing additional depth and breadth to existing indices and studies of related aspects of institutional design. To achieve this goal, the article (1) constructs an analytical framework that accounts for the institutional mechanisms that facilitate the direct and indirect representation of ethno‐national minorities across both macro‐level and micro‐level institutional designs in a state and (2) applies this framework to map institutional designs in twelve states to provide an indication of the usefulness of a new measurement tool (a representation index). The argument is that this framework and tool provide a corrective to the limitations of current approaches, advancing our ability to measure the institutional mechanisms of minority representation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".