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Impacting Grand Challenges: A 'Both/And' Approach

2020· article· en· W3046153062 on OpenAlexaff
Natalie Slawinski, Wendy K. Smith, Robin J. Ely, Tobias Hahn, Andrew J. Hoffman, Anita M. McGahan

Bibliographic record

VenueAcademy of Management Proceedings · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of TorontoMemorial University of Newfoundland
Fundersnot available
KeywordsScholarshipThrivingExpansiveGrand ChallengesEngaged scholarshipEngineering ethicsSociologyPolitical scienceTheme (computing)Work (physics)Public relationsEpistemologyEnvironmental ethicsSocial scienceComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

In this panel symposium, we seek to build on growing efforts by management scholars to engage with grand challenges and the United Nations’ Sustainable Development Goals (SDGs). Reflective of the All- Academy Theme description, we note that research and scholarship around addressing these important issues traditionally adopts an either/or approach, reflecting a false self-imposed dichotomy that prevents more expansive and synergistic thinking. To advance scholarship and insights about the management of grand challenges, we turn to paradox theory, an organizational lens gaining attention among organizational scholars that explores the nature of competing demands and unpacks approaches to move beyond either/or thinking into more both/and approaches. We structured this session to be an interactive panel discussion with scholars who have deep and rich knowledge of specific SDGs. They will both share their knowledge and engage in a robust and provocative discussion to address important questions about the role of both/and thinking to address grand challenges, and how management scholars can advance that work. We hope that bringing a paradox lens to these issues will deepen our scholarship, as well as help push forward on practices in academia that allow us to be more relevant and impactful in building a thriving and sustainable world.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.286
GPT teacher head0.387
Teacher spread0.101 · 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 designTheoretical or conceptual
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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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