Institutional Change as a Discovery Process Through the Development of Awareness
Bibliographic record
Abstract
Our understanding of institutional change has evolved considerably in recent years, moving from an entrepreneurial and agentic view to a more unintentional view of change. What is however lacking is a better understanding of the process leading from one to the other. This paper suggests that the two possibilities are on a continuum, institutional change being unintentional first, then becoming intentional as actors’ awareness rises. In this article, relying on a longitudinal study of an Algerian experience, we develop a process model of institutional change intentionality by showing how institutional actors’ intentionality develops as part of everyday practices within an organization. We provide two main contributions to institutional theory. First, we show the emergence of ‘institutional entrepreneurship’ from an intentionality point of view by showing how actors initially move from actions non-purposively targeting institutions to institutional work. Second, we identify a four stage-model of institutional entrepreneurship awareness. In doing so, we theorize how actors make sense of their daily organizational life and turn to deliberate institutional work.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 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".