Why Is There No Labor Party in the United States? Political Articulation and the Canadian Comparison, 1932 to 1948
Bibliographic record
Abstract
Why is there no labor party in the United States? This question has had deep implications for U.S. politics and social policy. Existing explanations use “reflection” models of parties, whereby parties reflect preexisting cleavages or institutional arrangements. But a comparison with Canada, whose political terrain was supposedly more favorable to labor parties, challenges reflection models. Newly compiled electoral data show that underlying social structures and institutions did not affect labor party support as expected: support was similar in both countries prior to the 1930s, then diverged. To explain this, I propose a modified “articulation” model of parties, emphasizing parties’ role in assembling and naturalizing political coalitions within structural constraints. In both cases, ruling party responses to labor and agrarian unrest during the Great Depression determined which among a range of possible political alliances actually emerged. In the United States, FDR used the crisis to mobilize new constituencies. Rhetorical appeals to the “forgotten man” and policy reforms absorbed some farmer and labor groups into the New Deal coalition and divided and excluded others, undermining labor party support. In Canada, mainstream parties excluded farmer and labor constituencies, leaving room for the Cooperative Commonwealth Federation (CCF) to organize them into a third-party coalition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".