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Recent Research on Paradox Theory: Emerging Themes and Mechanisms

2022· article· en· W4286620972 on OpenAlexaff
Arjun Odedra, Parshotam Dass

Bibliographic record

VenueAcademy of Management Proceedings · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCategorizationEpistemologyManagement scienceSociologyEngineering ethicsPsychologyEngineering

Abstract

fetched live from OpenAlex

Paradox theory is a bourgeoning area of interest among organization and management scholars. In this paper, we explore two overlapping research questions: (a) What are the major emerging issues in research on paradox theory; (b) What are the fundamental mechanisms that individuals and groups use to navigate paradoxes. From the reviewed literature, first, we delineate six major research themes and highlight important issues that researchers have addressed in their studies. Second, we propose a 2 × 2 × 2 matrix to categorize the mechanisms examined in the studies, and identify eight broad categories of mechanisms to navigate paradoxes in organizations. We hope our review will help integrate research findings and encourage theory testing in the future.

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)0.021
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.012
Science and technology studies0.0030.019
Scholarly communication0.0120.021
Open science0.0030.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.056
GPT teacher head0.300
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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