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Record W4396560261 · doi:10.1111/joms.13078

Grand Challenges Viewed through the Pragmatist Lens of the Economies of Worth: A Multidisciplinary Review and Framework for the Conduct of Moral Work in Pluralistic Settings

2024· review· en· W4396560261 on OpenAlexafffund
Charlotte Cloutier, Francis Desjardins, Linda Rouleau

Bibliographic record

VenueJournal of Management Studies · 2024
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPragmatismMultidisciplinary approachThrough-the-lens meteringWork (physics)Lens (geology)SociologyPositive economicsEconomicsEngineering ethicsEpistemologySocial sciencePhilosophyEngineering

Abstract

fetched live from OpenAlex

Abstract A fast‐growing number of organization and management scholars are responding to calls to conduct research on grand challenges (GCs). Few among these, however, question the core assumptions that underpin their efforts. In this paper we argue that the intractability of GCs stems from a failure to recognize the fundamentally pragmatic, plural, and moral character of these problems, which generate conflicts between groups over what is the ‘right’ or most appropriate course of action to pursue. A theoretical lens frequently used across many disciplines to make sense of problems such as these is Boltanski and Thévenot's (1991, 2006) economies of worth (EoW). On this premise, we undertake a multidisciplinary review of articles that use the EoW for studying GCs. Based on our analysis, we develop a pragmatist framework that articulates the practices that underpin the conduct of ‘moral work’ that organizational actors engage in as they seek to agree on a common sense of justice in GC contexts. Our framework provides a useful roadmap for scholars interested in applying a pragmatist perspective to our understanding of GCs, and by so doing, explore different, more socially just, and potentially more impactful ways of tackling them.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.751
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.147
GPT teacher head0.360
Teacher spread0.213 · 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
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

Citations8
Published2024
Admission routes2
Has abstractyes

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