The presence of citizen science in sustainability reporting
Bibliographic record
Abstract
Purpose Ongoing environmental threats have intensified the need for firms to take big leaps forward to operate in a manner that is both ecologically sustainable and socially responsible. This paper aims to assess the degree to which firms are adopting citizen science as a tool to achieve sustainability and social responsibility targets. Design/methodology/approach This study applies a qualitative content analysis approach to assess the current presence of citizen science in sustainability and social responsibility reports issued by Globescan sustainability leaders and by firms ranked by the Fortune 500 and Fortune Global 500. Findings While the term itself is mostly absent from reports, firms are reporting on a range of activities that could be classified as a form of “citizen science.” Practical implications Citizen science can help firms achieve their corporate sustainability and corporate social responsibility goals and targets. Linking sustainability and social responsibility efforts to this existing framework can help triangulate corporate efforts to engage with stakeholders, collect data about the state of the environment and promote better stewardship of natural resources. Social implications Supporting citizen science can help firms work toward meeting UN Sustainable Development Goals, which have highlighted the importance of collaborative efforts that can engage a broad range of stakeholders in the transition to more sustainable business models. Originality/value This paper is the first to examine citizen science in a corporate sustainability and social responsibility context. The findings present information to support improvements to the development of locally relevant science-based indicators; real-time monitoring of natural resources and supply chain sustainability; and participatory forums for stakeholders including suppliers, end users and the broader community.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".