A Regional Government for Fragmented St. Louis: Even the “Favored Quarter” Would Benefit
Bibliographic record
Abstract
In 1990, roughly three out of every four Americans lived in a metropolitan area. 1 When a traveler is asked where she is from, she is likely to say "Atlanta" or "Los Angeles," rather than naming either her state or the individual municipality in which she resides. 2 Her strong identification with the metropolitan region is based on daily experience: she likely lives in one locality, works in another, and spends time or money in several others.3 Like citizens, businesses operate beyond the immediate locality to find their customers, workers, and suppliers.4 Entertainment areas, cultural institutions, and natural resources are shared regionally.5 Yet the American system of local government ignores these realities.The lines of local government were set up for an earlier age, not today's highly mobile and interconnected society.6 The outdated system of local governments has left America's metropolitan regions ill-equipped to deal with modern challenges."[I]n most metropolitan regions the collective well-being of the region is not being pursued, primarily because of the aggregate spillover effects of local power being exercised by scores of autonomous localities, each without consideration of the impact of local decisions on the entire region."7 Legal 1. E.
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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 | 0.001 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.017 |
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".