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Record W4293072165 · doi:10.1017/bhj.2022.12

Gender and Intersectionality in Business and Human Rights Scholarship

2022· article· en· W4293072165 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBusiness and Human Rights Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsDalhousie UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntersectionalityScholarshipSociologyNeoliberalism (international relations)Privilege (computing)Gender studiesRacismPolitical sciencePolitical economyLaw

Abstract

fetched live from OpenAlex

Abstract In this article, we explore what intersectionality, as an analytic tool, can contribute to business and human rights (BHR) scholarship. To date, few BHR scholars have explicitly engaged in intersectional analysis. While gender analysis of BHR issues remains crucial to expose inequality in business activity, we argue that engagement with intersectionality can enrich and support this and other BHR scholarship. Intersectional approaches allow us to move beyond single-axis analysis, contest simplistic representations about gender issues and expose the complexity of human relations. It draws our attention to structures that sustain disadvantage such as racism, colonialism, social and economic marginalization and systematic discrimination. Moreover, intersectionality emphasizes the need to centre the contributions of those who have been marginalized. It can be used to challenge the legitimacy of the state and support subaltern, decolonized or postcolonial, including indigenous, perspectives. Adopting an intersectional approach can help problematize the neoliberal capitalist system and its constructs, in which the BHR normative framework is embedded, calling into question the reification of economic growth and its impact on individuals, communities and the planet. We must, however, remain cautious of attempts to co-opt intersectionality in the service of neoliberalism and remain conscious of our own privilege and discursive practices.

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 categoriesScience and technology studies, Insufficient 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 score0.675
Threshold uncertainty score0.999

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 studies0.0110.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.059
GPT teacher head0.318
Teacher spread0.259 · 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