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Developing Inclusive Innovations to Address Institutional Failures

2019· article· en· W2966331762 on OpenAlexaff
Nilanjana Dutt, Gerard George, Sérgio G. Lazzarini, Johanna Mair, Anita M. McGahan

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConversationVulnerability (computing)PovertySocioeconomic statusPublic relationsEntrepreneurshipProperty rightsBusinessPolitical scienceSociologyEconomicsEconomic growthFinanceComputer science

Abstract

fetched live from OpenAlex

The panel session will support a conversation among leading scholars studying inclusive innovation and institutional theories. We aim to do so by bringing together researchers interested in enhancing our understanding about organizations’ strategies to solve unstructured problems that affect disenfranchised populations. In particular, this class of problems is usually present in institutionally flawed settings, which have ‘voids in commercial institutions’ (Dutt et al., 2016). These institutional misalignments can be expressed in terms of weak property rights, less developed capital markets, less established formal job markets, and higher risk of poverty vulnerability. To that end, the panel’s goal is to improve our knowledge of how inclusive organizations might help overcome institutional misalignments in low-income locations, fostering socioeconomic development. We believe that the panel is aligned with the main theme of the 2019 Academy of Management Conference, “Understanding the Inclusive Organization,” in particular with the following divisions: Strategic Management (STR); Entrepreneurship (ENT); and Social Issues in Management (SIM).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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.

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
Published2019
Admission routes1
Has abstractyes

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