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Record W2562020581 · doi:10.4135/9781446280669.n2

Organizational Legitimacy: SixKey Questions

2017· book-chapter· en· W2562020581 on OpenAlexaff
David L. Deephouse, Jonathan Bundy, Leigh Plunkett Tost, Mark C. Suchman

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLegitimacyKey (lock)Political scienceComputer scienceLawComputer securityPolitics

Abstract

fetched live from OpenAlex

Legitimacy is a fundamental concept of organizational institutionalism. It influences how organizations behave and has been shown to affect their performance and survival (Pollock & Rindova, 2003; Singh, Tucker, & House, 1986). As developed in organizational institutionalism the term has spread widely across the social sciences, and because of this, our current understandings of legitimacy and how it is managed are much more nuanced and elaborate than portrayed in early institutional accounts. In this chapter, we seek to bring greater clarity and order to the growing and sometimes confusing literature, focusing on the conceptualization of legitimacy itself and how it changes over time.This chapter builds from the previous edition (Deephouse & Suchman, 2008, available online at www.sage.org/organizational institutionalism/legitimacy). In updating that chapter we reviewed 1299 publications and conference papers that had the string “legitim” in the title, abstract, or keywords. Reflecting the reach and power of legitimacy, these publications included books and a wide range of journals and across a wide range of disciplines (e.g., communication, political science, public administration, and sociology -- not just management). Our goal was both to identify both broad trends in theory and research and possible theoretical innovations and also to highlight important applications for scholars in organizational institutionalism. From this review we identified six central questions around which this chapter is arranged: What is organizational legitimacy? Why does legitimacy matter? Who confers legitimacy, and how? What criteria are used (for making legitimacy evaluations)? How does legitimacy change over time? These questions are shown in Figure 1.1. Our final section asks “Where do we go from here?” and offers suggestions for future research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0040.024
Scholarly communication0.0140.027
Open science0.0020.006
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.020
GPT teacher head0.215
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations674
Published2017
Admission routes1
Has abstractyes

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