LIFE AND DEATH OF AN INSTITUTION: THE CASE OF COLLECTIVE WHEAT MARKETING IN AUSTRALIA
Bibliographic record
Abstract
Since the 1980s, there has been renewed interest among political scientists in the role of institutions. An important strand of this ‘new institutionalism’ is historical institutionalism. Recent theoretical developments have sought to address the most obvious criticisms of the historical institutionalist approach, particularly the critique relating to its tendency to focus on explanations of stability. However, the institutional histories under consideration are generally incomplete as they do not include the entire life of the institution. Drawing on the history of collective wheat marketing in Australia, this article seeks to address this gap by considering a case in which strategies of institutional reproduction initially appeared to have managed change successfully but ultimately sowed the seeds of institutional demise. By considering the death as well as the life of an institution, we can gain a clearer picture of the effectiveness of institutional strategies of adaptation and change.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.029 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| 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".