Bibliographic record
Abstract
The Supreme Court’s certiorari process is a barrier to justice for parties like Indian tribes and individual Indians. Statistically, there is a near zero chance the Supreme Court will grant a certiorari petition filed by tribal interests. At the same time, the Court grants certiorari in more than a quarter of petitions filed by the traditional opponents to tribal sovereignty, states. Why? The Supreme Court has long maintained that the certiorari process is a neutral and objective means of eliminating patently frivolous petitions from consideration. This empirical study of preliminary memoranda drafted by the Supreme Court law clerk pool demonstrates the likelihood that the Court’s certiorari process is neither objective nor neutral. Cert pool clerks overstate the relative merits and importance of petitions filed by states against tribal interests, while understating the merits and importance of tribal petitions. In this study of more than 162 certiorari petitions filed between 1986 and 1994, a majority of petitions brought by state and local governments received favorable treatment from the cert pool while recommending denial in all but a single tribal petition, often labeling them “factbound” and “splitless.” The impact of this weighted review of cert petitions is that a disproportionate number of state government petitions are granted while very few tribal petitions are granted. ∗ Associate Professor, Michigan State University College of Law. J.D., University of Michigan, 1997. Enrolled Citizen, Grand Traverse Band of Ottawa and Chippewa Indians. Thanks to Bethany Berger, Kristen Carpenter, Kirsten Carlson, Rick Collins, Richard Delgado, Zeke Fletcher, Phil Frickey, Kate Fort, Rick Garnett, Ian Gershengorn, David Getches, Brian Kalt, Sarah Krakoff, Riyaz Kanji, Ellen Katz, Sonia Katyal, Alexa Koenig, Beth Kronk, Maria Pablon Lopez, John Low, Lou Mulligan, Meg Noori, John Petoskey, Angela Riley, Judy Royster, Wenona Singel, Joe Singer, Alex Skibine, Harold Spaeth, Lee Strang, David Stras, Gloria Valencia-Weber, and Rob Williams for their comments and assistance on this paper. Thanks also to the participants at talks held at the University of Michigan Law School, the University of Colorado Law School, and at the Third Big Ten Untenured Scholars’ Conference at Indiana University School of Law.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".