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Resourcing for Inclusion of Marginalized Actors in Transnational Governance

2017· article· en· W2766159367 on OpenAlexaff
Natalia Aguilar Delgado, Paola Perez-Aleman

Bibliographic record

VenueAcademy of Management Proceedings · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsDisadvantagedContext (archaeology)Inclusion (mineral)Corporate governancePublic relationsNegotiationSociologyPolitical scienceInstitutionIndigenousBusinessSocial scienceLaw

Abstract

fetched live from OpenAlex

Members of many communities around the world work daily to become active participants in changing or building transnational regulations, despite limited access to the resources commonly required for engagement in these processes. The research question at the center of this study is: how marginalized actors work to get included in transnational governance? This issue was examined in the context of the construction of a new regulation in the United Nations, the legally-binding Nagoya Protocol designed to regulate access and benefit-sharing (ABS) initiatives in the context of biodiversity-based innovation. This study focuses on indigenous peoples - non-state actors who historically have been excluded from policy-making processes and are in a disadvantaged position for contributing to the shaping of this new institution. This research project adopts a qualitative, inductive and longitudinal research strategy in a multi-event ethnography. The main finding highlights the emergence of the mechanism “resourcing”, composed of three different types of resourcing: organizational, discursive and material. Building on this finding, the paper elaborates a grounded model for inclusion in transnational regulation creation that illustrates the continuous interplay among “negotiation spaces”, “positions” and “resourcing”. This paper contributes to the literature by illuminating the practices behind inclusion of marginalized actors involved in transnational governance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.358

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 communication0.0000.000
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 head0.018
GPT teacher head0.283
Teacher spread0.265 · 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 designObservational
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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