MétaCan
Menu
Back to cohort

Fostering True Self-Expression in Organizations

2024· book-chapter· en· W4392085242 on OpenAlexaff
Patricia Faison Hewlin, Laura Morgan Roberts

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsMcGill University
Fundersnot available
KeywordsExpression (computer science)PsychologyPolitical scienceComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Abstract Authenticity, which has been conceptualized as both a trait and a state influenced by the context, is crucial for understanding individual differences in organizations. This chapter begins by exploring the meaning of authenticity, its relevance to individual differences in organizations, and the relationship between trait and state authenticity. It then summarizes key findings from empirical research on authenticity, which highlights the difficulty of expressing one’s whole self —including one’s distinctive attributes—in organizations. It also reveals the need for research on tensions or trade-offs associated with authenticity. Addressing this gap, the chapter develops a conceptual framework of leader authenticity tensions—challenges that leaders face when they seek to foster their own and other people’s authenticity in the workplace. The framework includes metaphors to help illuminate this complex phenomenon. The chapter also identifies strategies that may help leaders to navigate the tensions successfully and suggests directions for future research on authenticity in organizations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.993
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.245
Teacher spread0.212 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicKnowledge Management and SharingFrench-language works237,207