Nature or nurture? Agency life‐cycles as a function of institutional legacy, political environment, and organizational hardwiring
Bibliographic record
Abstract
Abstract A growing body of literature attempts to explain the life‐cycles of public sector organizations. Of particular interest have been the form and incidence of their birth and termination, and connecting these events to such variables as legal status and political ideology. Less attention has been given to the effect of intermediary life‐cycle events, the tasks performed by agencies, and their policy domains. This study builds on existing fixed characteristics (nature) and dynamic environmental (nurture) approaches and uniquely supplements them with a new institutional legacy paradigm that examines how previous organizational reforms influence future reform. Moreover, we advance existing studies by providing more comprehensive tests of the role that task type and policy domain play. Finally, we retest “classic” nature and nurture variables, namely, political turnover and legal form. Results suggest that nature and nurture provide important pieces of the organizational life‐cycle puzzle and that nurture comprises both external and intra‐organizational dynamics.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".