A Reply to Fenna and Schnabel’s (2024) “What is Federalism?”
Bibliographic record
Abstract
Alan Fenna and Johanna Schnabel (2024; henceforth F&S) want to clarify what federal scholars mean when they study their object. They identify “two main camps” (p. 181), one resting on co-determination and the other on autonomy, siding with the latter. In so doing, they provide a definition of federalism that sees in “the autonomy of two orders of government the essential, defining, element” (p. 2; original emphasis). They specify this further to mean “the existence of two orders of government with constitutional status, each enjoying a direct relationship to the people with meaningful powers, and whose status and powers are constitutionally protected” (p. 191). This reply highlights three main problems with this definition. To begin with, although the title of their article and several instances therein speak of “federalism,” the goal of F&S is actually to characterize federal states, that is, “to clearly distinguish … federations from unitary states … and … unions so loosely joined as not to be states at all” (pp. 179–80). This also becomes evident in their use of statist terminology (“governments,” “constitution,” “referendums,” “law making,” etc.). In terms of this question, their minimalist definition is entirely logical. Yet, it is still difficult to operationalize and thus not as helpful as they think it is, for the following reasons.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.040 | 0.043 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".