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Record W3040990482 · doi:10.18192/potentia.v8i0.4435

International Institutions, Global “Partnerships” and the Structural Power of Multinational Corporations

2017· article· en· W3040990482 on OpenAlexaffvenue
Mark Machecek

Bibliographic record

VenuePotentia Journal of International Affairs · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGlobal governanceMultinational corporationAgency (philosophy)Corporate governancePolitical sciencePublic administrationMandatePolitical economyHuman rightsPoliticsSociologyEconomicsLawSocial scienceManagement

Abstract

fetched live from OpenAlex

Throughout the last two decades, institutions of global security and governance have undergone a paradigmatic shift in their engagements with multinational corporations (MNCs). The United Nations, in particular, has increasingly embraced big business as “partner” in human security, humanitarian response and development through formalized “global public–private partnerships” (GP3s). Naturally, a debate has emerged on the efficacies of these GP3s and their implications for global governance. This paper contributes to this debate by proposing and employing a new research agenda that interrogates the impacts that GP3s have on international institutions themselves using a case study of a particular UN agency, the United Nations High Commissioner for Refugees (UNHCR). It will argue that UNHCR GP3s are a highly asymmetrical set of power relations that are having constitutive effects on the agency. The UNHCR is undergoing significant operational and ideological changes in the GP3 process in a manner that is synonymous with Stephen Gill’s (1998) concept of “new constitutionalism”; a reconstitution that opens up and further embeds the agency within the forces of the capitalist global political economy. Hence, this case study demonstrates that GP3s are capable of undermining the mandate and autonomy of global security and governance institutions.

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)0.002
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: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.032
GPT teacher head0.342
Teacher spread0.309 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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