Gender and Intersectionality in Business and Human Rights Scholarship
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.012 | 0.090 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".