The contextualist turn and schematics of institutional fit: Theory and a case study from Southern India
Bibliographic record
Abstract
The policy literature has long recognized the inherent need for a program to fit the unique conditions found in a certain context. We present a theory of institutional contextualism that focuses on the mechanisms by which actors adapt a policy design to fit a situation. We conceptualize institutions as phenomena that are constituted by a constant dialectic between text (the general blueprint) and context (the particular setting). The first half of this dialectic, which is the diffusion of the constitutive text or norm onto the institutional setting, has been discussed in the literature. Our research focuses on the second half, and we delineate, in concept, mechanisms for fitting the program to the local context. We then use a case study of improvised microfinance programs in Tamil Nadu, India, to illustrate how this occurs in reality. The research underscores the unexamined link between effective governance and contextual fit and offers a typology of mechanisms for fit that should inform future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".