The “Macro” and the “Micro” of Legitimacy: Toward a Multilevel Theory of the Legitimacy Process
Bibliographic record
Abstract
The distinction of macro- and microfoundations of institutions implies a multilevel conceptualization of institutional processes. We adopt the evaluators' perspective on legitimacy to develop a multilevel theory of the legitimacy process under ideal-type conditions of institutional stability and institutional change, and we explore the dynamics of institutional change—from destabilization of the institutional order to return to stability in legitimacy judgments expressed by evaluators. We argue that through the process of institutionalization, legitimacy judgments of evaluators are subjected to social control and describe an institutional stability loop—a cross-level positive-feedback process that ensures persistence of legitimacy judgments and stability of the institutional order. Viewing institutional stability as a state of suppressed microlevel diversity, we draw researchers' attention to “silenced” legitimacy judgments and to judgment suppressor factors that induce evaluators to abstain from making their deviant judgments public. The removal of such factors leads to the (re)emergence of competing judgments in public communications and creates an opportunity for institutional change. We explore competitive strategies that address propriety or validity components of legitimacy and describe the process through which organizational fields return to a state of institutional stability.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.003 | 0.040 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".