The Global Compact and its concrete effects
Bibliographic record
Abstract
Purpose The purpose of this study is to document the concrete practices put in place by United Nations Global Compact (UNGC) affiliated firms and their application of the UNGC Communication on Progress (COP). Design/methodology/approach The paper examines the practices implemented by firms on the Fortune 500 list that have affiliated with the UNGC and issued a COP separate from their annual report or a sustainable development report. According to the UNGC, the COP policy sets out a description of practical actions the company has taken or plans to take to implement the ten principles. Findings The findings tend to show that firms affiliated with the UNGC use a variety of practices to integrate these principles. Many adopt policies based on an international standard relating to a UNGC principle. However, the reporting process supported by the UNGC does not seem to fully promote the widespread application of these practices. Originality/value The documentation of these practices will serve as a reference for any business interested in adopting the UNGC principles or for government and non-government organisations, including accounting standard setters, aiming to promote and support the universal principles on human rights, labour, the environment and anti-corruption. In addition, the study reveals weaknesses in the UNGC COP policy that could limit more extensive application of these practices.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".