The Politics of Social Learning: Finance, Institutions, and Pension Reform in the United States and Canada
Bibliographic record
Abstract
Because the traditional concept of social learning has faced significant criticism in recent years, more analytical work is required to back the claim that the lessons drawn from existing institutional legacies can truly impact policy outcomes. Grounded in the historical institutionalist literature, this article formulates an amended concept of social learning through the analysis of the relationship between finance, social learning, and institutional legacies in the 1990s debate over the reform of earnings‐related pension schemes in the United States and Canada. The article shows how social learning related to specific ideological assumptions and policy legacies in the public and the private sectors has affected policymaking processes. At the theoretical level, this contribution stresses the political construction of learning processes, which is distinct from the technocratic model featured in the traditional literature on social learning. This article also distinguishes between high‐ and low‐profile social learning while emphasizing the impact of private policy legacies on learning processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".