Bibliographic record
Abstract
An after dinner speaker should stimulate the mind without disturbing the digestion.The title of my talk is "Federalism with Two Languages."I hope the topic of federalism will stimulate the intellect, and I hope to succeed in discussing two languages without disturbing anyone's digestion.Federalism is a system of sharing governance at difference levels, especially the federal or central level, and the provincial or state level.Canada, the United States, andGermany are examples of federal systems.In contrast to federalism, the unitary state is a system with a single government that divides the nation into departments for purposes of administration.The departments do not have their own governors, merely administrators.France and Japan are usually cited as examples of unitary states.Federalism is a prominent system in the 20 th century that I expect to become prominent in the 21 st century.Alexis de Toqueville made penetrating observations of the young American republic, which helped to create its conception of itself.Speaking of the United States de Toqueville said "the federal system was created with the intention of combining the different advantages which result from the magnitude and littleness of nations."The economic analysis of law reformulates de Toqueville's problem as "finding the optimal number of governments.2 I will explain this general problem and then relate it to a country with more than one linguistic group.The Optimal Number of Governments 1 I wish to thank Chris Swain for transcribing and editing this lecture.2 I introduced this phrase in Part 2 of my book, The Strategic Constitution (2000), upon which this lecture draws.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".