Organizations' engagement with sustainable development goals: From<scp>cherry‐picking</scp>to SDG‐washing?
Bibliographic record
Abstract
Abstract This article analyzes the organizational engagement with the United Nations sustainable development goals (SDGs), an initiative for corporate social responsibility also referred to as the 2030 Agenda. Engagement with the SDGs by organizations all around the world, whatever their sector and size, has attracted a lot of media interest and heightened expectations. Nevertheless, there is a lack of empirical work that sheds light on the commitment to this initiative at the organizational level. In order to fill this gap, this article examines the characteristics of engagement with the SDGs of 1370 organizations from 97 countries, taking data from their sustainability reports. The study looks at how and why organizations engage with the SDGs, as well as the priority they assign to them. The findings point to a superficial engagement with the SDGs for the vast majority of organizations, which suggests a process of “SDG‐washing”. Implications for managers, public policy makers and other stakeholders are analyzed.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".