FROM GERRY‐BUILT TO PURPOSE‐BUILT: DRAWING ELECTORAL BOUNDARIES FOR UNBIASED ELECTION OUTCOMES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electoral bias causes unfair election results that call into question the legitimacy of governments and undermine confidence in the integrity of the electoral system. Biased electoral outcomes in the UK, Canada and Australia are often accepted as an inherent function of a single‐member system, not remediable by independent boundary commissions because they are required to work without taking partisan considerations into account. Meanwhile the US Supreme Court cannot decide on a point beyond which bias should be struck down. Those who do not accept the inevitability of unfair election outcomes see proportional representation as the solution, but the cost—loss of the representational value of single member districts—makes this a contestable solution. But if boundaries could be drawn to produce an unbiased electoral system and generate fair electoral outcomes, single‐member districts could be retained and confidence could be returned. South Australia requires its independent boundaries commission to do just that, and the results indicate that fair electoral outcomes can indeed be produced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it