Woodrow Wilson's Place in Political Time: A Critique of Stephen Skowronek's The Politics Presidents Make
Bibliographic record
Abstract
In Stephen Skowronek'sThe Politics Presidents Make, Skowronek divides all past U.S.presidents into one of four categories in his typology.He spends his entire discussion on three of these categories: the reconstructives, who attempt to repudiate the past to establish a new party regime, the articulators, who act as the faithful sons and continue the commitments of the reconstructives, and the disjunctives, who struggle with the impossible leadership situation to revive the dying regime.The preemptives, those who come into power opposed to a resilient regime, are hardly discussed in his entire analysis.Skowronek defines these presidents as "the wild cards of presidential history," given that they do not fit in his recurrent pattern of foundation, consolidation, fragmentation, and decay. 1 He claims that the preemptives do not "establish, uphold, or salvage" like the other three groups, but instead, offer a third way, or an alternative.2 This classification, as well as his lack of discussion on the preemptives, makes it seem as though there is no place for the preemptive presidents in his typology.Skowronek not only seems to disregard their significance, but also does not consider long-term effects of opposition presidents.He effectively isolates and removes the "wild card" preemptives of his own creation completely from political time.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.060 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 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".