How Ideas Change and How They Change Institutions: A Memetic Theoretical Framework
Bibliographic record
Abstract
The ideational turn in institutionalism, arguably entering its third decade (with precursers as early as Kingdon 1984), led to a diverse literature on how ideas change institutions. However, this literature largely treats ideas themselves as static, which results in many of the same problems encountered by historical institutionalism transposed to the realm of ideas – e.g., reliance on critical junctures and “heroic agency” as central to the dynamic of change.This paper combines insights from discourse theory and evolutionary theory to argue that reimagining ideas as independent agents in the spirit of Richards Dawkins’ “memes” enables the student of institutional change to avoid this pitfall, and opens the way for a new methodology, combining process tracing and discourse analysis, that follows the development of ideas themselves and not the people who hold them. Rather than indicating a flaw in previous arguments, this paper suggests that new insights can be infused into existing theories by taking the perspective of ideas, here seen as the building blocks of agency.Ideas are described as a network wherein each node is partially defined by the nodes it is connected to. Evolutionary changes to the network result in the change of meaning. New nodes attempting to infiltrate the network require anchor points they can latch on to while uncoupling contradictory nodes. Institutions, which are snapshots of a subset of the ideational field that persevere while the network itself continues to fluctuate, are impervious to these continued changes, but only to a certain degree. Substantial shifts in the core ideas of an institution will lead to change in the institution itself. Such shifts, in turn, can only be achieved by attacks from the periphery that slowly undermine links between core ideas until they can be pitted against one another, to the benefit of contending peripheral ideas seeking a more central position.This model can be used to explain both changes purportedly caused by exogenous shocks and processes of endogenous change such as those described by Streeck and Thelen (2005), without resorting to agency as a black box within which much of the action actually takes place. The model also explains the strong historical contextualization of any institutional change, and why some exogenous shocks may fail to initiate change while others succeed. In this manner, ideas reacquire their dynamic nature and change in policy making becomes endogenous to the policy making process.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.081 |
| Scholarly communication | 0.019 | 0.031 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.005 |
| 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".