MétaCan
Menu
Back to cohort
Record W4383721320 · doi:10.1017/bhj.2023.27

Downstream Human Rights Due Diligence: Informing Debate Through Insights from Business Practice

2023· article· en· W4383721320 on OpenAlex
Benn F. Hogan, Joanna Reyes

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.

Bibliographic record

VenueBusiness and Human Rights Journal · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsTrinity College
Fundersnot available
KeywordsDue diligenceDownstream (manufacturing)Human rightsMultinational corporationBusinessDirectiveSustainabilityCorporate social responsibilityPublic relationsPolitical scienceLawMarketingFinance

Abstract

fetched live from OpenAlex

Abstract The United Nations Guiding Principles on Business and Human Rights conceive of human rights due diligence (HRDD) as covering potential impacts across value chains, including downstream. The proposed EU Corporate Sustainability Due Diligence Directive and the revision process of the OECD Guidelines for Multinational Enterprises have sparked renewed discussion on how and whether companies should conduct HRDD downstream to identify and prevent or mitigate adverse human rights impacts. Whilst some debate has occurred previously on downstream HRDD, this has predominantly centred on specific sectors, products and services where the links to egregious human rights harms may be more readily identifiable. This piece seeks to inform the current debate by broadening the examples of sectors, products and services and current business practice which demonstrate the critical need for, and ability of, companies to consider human rights risks downstream.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0070.000
Scholarly communication0.0030.008
Open science0.0010.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.289
Teacher spread0.262 · 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