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Solving Societal Grand Challenges: A Debate and Future Directions

2024· article· en· W4400440939 on OpenAlex
Paul S. Adler, Ruth V. Aguilera, Rodolphe Durand, Gerard George, Olga Hawn, Dovev Lavie, Anita M. McGahan, Kerstin Neumann, Sérgio G. Lazzarini

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Proceedings · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Systems and Global Transformations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGrand ChallengesPolitical scienceEnvironmental ethicsEngineering ethicsPhilosophyEngineeringLaw

Abstract

fetched live from OpenAlex

Despite increasing attention of management scholars to the study of societal grand challenges and the progress made with promoting sustainability programs in the private sector, societal challenges such as economic inequality, public health hazards, climate change, and social divide, have worsened. The purpose of this symposium is to bring together leading scholars to engage in a debate and discuss their views and research concerning established and emerging approaches in management research for solving societal grand challenges. The panelists will identify advantages, limitations and contingencies of focused change interventions and broad system changes as well as debate the private versus public responsibilities for solving societal grand challenges, while considering new forms of economic systems and organizational governance. Thus, the panelists will share diverse views on the topic and deliberate with the audience about emerging perspectives, practices, and promising avenues for future research on solutions to societal challenges.

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.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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