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Conspicuously Imperceptible: The Moderating Role of Mindfulness in the Experience of Paradoxical Priorities

2019· article· en· W2966130784 on OpenAlexaff
Xiaoxi Chang, Susan E. Brodt

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsQueen's University
Fundersnot available
KeywordsMindfulnessScholarshipPsychologyLeverage (statistics)Social psychologyEpistemologySociologyPsychotherapistPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Organizations are rife with paradox, or seemingly contradictory yet interdependent forces that persistently surface, often across organizational levels (Smith & Lewis, 2011). This paper aims to answer the recent call for greater scholarly attention to the micro- foundations of organizational paradox (Miron-Spektor, Keller, Ingram, Lewis, & Smith, 2018; Schad, Lewis, Raisch, & Smith, 2016). In two experimental studies, we conceptually and empirically investigate what people think and how they feel when tackling paradoxical demands. Building upon the burgeoning body of research on mindfulness - the degree to which one brings full attention and awareness to the present in a non-judgmental way (Baer et al., 2006; Brown & Ryan, 2003) – we further explore when, how and why highly mindful individuals could benefit from seeming contradictions. Our results reveal that tasks entailing competing priorities may lead to changes in one’s integrative complexity, interfering thoughts, and calming emotions, as a function of trait mindfulness (i.e., high versus low). Overall, mindfulness fosters and maintains positive psychological experiences in the face of paradox. This paper informs not only the paradox and mindfulness scholarship but also practitioners about the peculiar nature of paradox and strategies to leverage competing work priorities.

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.244
Teacher spread0.232 · 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 designObservational
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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