Dynamic Persistence of Institutions: Modeling the historical endurance of Red Vienna’s public housing utopia
Bibliographic record
Abstract
How does a daring idea such as the utopia of affordable housing weather a century of change? The persistence of institutions—shared meanings that shape individual actions—is a central feature of organizational life. Recent scholarship stresses that institutions endure not because they are static but because they evolve as individuals maintain them. However, the search for micro-foundations has sidelined the macro-conditions of such dynamic persistence . Building on structural studies of meaning, we propose and illustrate a new, complementary theory-method package that can reveal how ideas are embedded and evolve in meaning structures. Dynamic modeling of discourse (DMD) tracks changing cultural meanings over time, doing justice to the assumption that institutional persistence can result from fluid changes in how institutions are instantiated as observable patterns of interactions at any given time. We develop three diagnostic measures for tracking both institutions and their instantiations in large corpora. Applying DMD to a 140-year corpus of reports of the City of Vienna, Austria, we show that the persistence of public housing as an institution was possible due to periodically changing instantiations—such as whether public housing policies subsidize landlords or tenants—with shifting affiliations to broader meanings. Our paper unlocks methodological doors to a dynamic, contextual approach to studying institutions that complements archival and ethnographic methods. It allows researchers to test theory-led expectations about persistence and provides a mixed-methods tool for historical research on organizations. We conclude with implications for structural studies of meaning, persistence, and change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".