Toward a More Critical and “Powerful” Institutionalism
Bibliographic record
Abstract
While we agree with the spirit and much of the content of Munir’s critique of “institutional theory,” it is important to note at the outset that recent developments—especially the rise of the institutional logics perspective (Thornton, Ocasio, & Lounsbury, 2012)—provide new opportunities to embrace more critical traditions and notions of power and domination while also uncovering alternative possibilities for examining socioeconomic changes. Another caveat is that given the panoply of theoretical and empirical conversations that cir-culate under the banner of “institutional theory” (Greenwood, Oliver, Suddaby, & Anderson, 2008), it has become difficult to even understand what it is, so to levy a credible critique. Nonetheless, it is a reasonable concern that Institutional the-ory’s treatment of organizations and the world they operate in tends to equate power with legitimacy (Scott, 2013). Munir’s concern about its seeming indifference to serious problems related to power and inequality is important because institutional theory is such a powerful referent and reference in organizational scholarship.Starting with the presumption that if they do not embody legitimacy, organizations cannot be powerful, institutional theory has not examined how they became powerful, or things they do to remain so (Hirsch, 1997). Instead, its stud-ies emphasize the culture and symbols they reflect (always taken to be positive), which in turn reinforce their legiti-macy. Charles Perrow criticized this focus on the
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.007 | 0.098 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".