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
Gerrymandering is returning to the Supreme Court. 1 For the first time in three decades, a federal court invalidated redistricting legislation on the grounds that it constituted a partisan gerrymander in violation of the Fourteenth Amendment. 2That court relied, in part, on a new tool-the efficiency gap-which some have touted as the means to "end gerrymandering once and for all." 3 We evaluate this tool and find it wanting.The efficiency gap is neither a cure to the malady of partisan gerrymandering nor even a good idea.Its use by courts may worsen the problem they seek to solve.The foundation for partisan gerrymandering claims is the 1986 case of Davis v. Bandemer, 4 in which a fractured Supreme Court held that partisan gerrymandering is justiciable as a violation of the Equal Protection Clause of the Fourteenth Amendment because it is an attempt to weaken the voting power of a disfavored political party.The ruling has been repeated in every Supreme Court case on partisan gerrymandering since, despite a minority view that partisan gerrymandering should be held to be a political question. 5However, while these cases have opened the door to challenges, neither Bandemer nor any of the subsequent cases rejected any districting laws as constituting an illegal partisan gerrymander.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.038 |
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