Bibliographic record
Abstract
This Article asks whether the openness to court-packing expressed by a number of Democratic presidential candidates (e.g., Pete Buttigieg) is democratically defensible. More specifically, it asks whether it is possible to break the apparent link between demagogic populism and court-packing, and it examines three possible ways of doing this via Bruce Ackerman’s dualist theory of constitutional moments—a theory which offers the possibility of legitimating problematic pathways to constitutional change on democratic but non-populist grounds. In the end, the Article suggests that an Ackermanian perspective offers just one, extremely limited pathway to democratically legitimate court-packing in 2021: namely, where a Democratic President and Congress would be willing to limit themselves to using court reform as a means of repudiating the Republican Party’s constitutional gains but not as a means of pursuing (in fact or in appearance) their own comprehensive reform agenda. The question that this analysis leaves hanging is whether this pathway remains satisfactory when concerns aside from democratic legitimacy are factored into the equation, such as a concern with the protection of certain fundamental rights, or with the possibility of public and institutional backlash against court-packing.
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.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".