Social Evaluations: The Interplay Between Authenticity and Legitimacy
Bibliographic record
Abstract
This article explores the complex interplay between the concepts of legitimacy and authenticity within organizational settings, addressing the conceptual overlap and ambiguities arising from their isolated study. Legitimacy, traditionally viewed as the generalized perception of an entity's appropriateness within a social system, and authenticity, perceived as the genuineness of an entity, share intertwined evaluation processes driven by audience judgments grounded in shared norms and values. However, the distinct yet overlapping nature of these constructs often leads to conceptual confusion, challenging the accumulation of empirical knowledge. By adopting a unified theoretical framework, this paper seeks to delineate the boundaries and interactions between legitimacy and authenticity more clearly. It employs a multiple audiences model to illustrate how diverse audience segments with varying values influence these social evaluations, particularly in contested environments. This model underscores how entities navigate the dual pressures of conforming to institutional expectations while maintaining authenticity in their identity and practices. The framework aims to clarify how these constructs co-evolve and impact organizational outcomes, providing new insights into the dynamic processes that underpin social evaluations in organizational contexts. Through this exploration, the paper contributes to a more nuanced understanding of legitimacy and authenticity, proposing ways to reconcile the tensions between them and enhance theoretical and empirical precision in management studies.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".