Being Your True Self at Work: Integrating the Fragmented Research on Authenticity in Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In tandem with a surge of public interest in authenticity, there is a growing number of empirical studies on individual authenticity in work settings. However, these studies have been generated within separate literatures on topics such as authentic leadership, emotional labor, and identity management, among many others, making it difficult for scholars to integrate and build on the authenticity research to date. To facilitate and advance future investigations, this article reviews the extant empirical work across 10 different authenticity constructs. Following our research review, we use a power lens to help synthesize our major findings and insights. We conclude by identifying six directions for future research, including the need for scholars to embrace a multifaceted view of authenticity in organizations. Overall, our review both reinforces and tempers the enthusiasm in contemporary discussions of authenticity in the popular and business press.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it