Shifting Logics: Erosion of Appropriateness and Knowledge Uptake of Rules
Bibliographic record
Abstract
Persistence and shifts in logics of action are difficult and under- explored topics, yet they are extremely important for theory development in the social sciences, and in organization theories in particular. In this paper we focus on the Carnegie logics of appropriateness and consequences (LoA and LoC) and explore mechanisms that drive (and impede) logics shifts. We argue that rule-based logics (LoAs) evolve over time as the rules that define appropriateness are replaced with revised rules. Our core claim is that myopic learning processes deposit imperfections into rules that erode their appropriateness and thereby lead to rule revisions that adjust the knowledge encoded in rules (knowledge uptake revisions). We explore this claim empirically with longitudinal data of rule changes in a health care organization. Our results suggest that the knowledge uptake of rules significantly depends on mechanisms that erode the appropriateness of the rules. The general implication is that erosion of appropriateness drives the persistence and shifts in logics, and we think it should be studied in more detail in the future.
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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 | 0.016 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".