How Accounting on the Sustainable Development Goals (SDGs) Contribute to Politicize Corporations: A Case Study
Bibliographic record
Abstract
Purpose: The purpose of this research is to understand the mechanisms through which accounting on the Sustainable Development Goals (SDGs) contributes to transform corporations into political actors eager to influence activities that are usually the prerogative of the states. Design/methodology/approach: The article is informed by an 18-month intervention research in a European listed corporation. The project consisted in implementing the first accounting system for the SDGs for the corporation. Data collection involved extensive participative observation, informal interviews and documentary evidence. Findings: The article uncovers three mechanisms through which accounting on the SDGs contributes to the politicization of the corporation: 1) by shaping the political leadership of the organization; 2) by accounting for sustainable development through the creation of an accounting system based on the SDGs and 3) by setting the boundaries of the political engagement of the organization. Originality/value: The contribution of the article is twofold. Firstly, it addresses several calls for understanding the impact of SDGs on corporate practices and enriches previous research on accounting for sustainable development by elaborating on the processes through which accounting could contribute to achieve such goals. It also identifies the specificities linked to such form of accounting and the associated difficulties for corporations. Secondly, it contributes to the literature on political CSR by providing some insights into the operationalization of the politicization of corporations. In doing so, it uncovers some differences between being engaged in political CSR actions and becoming a political actor. Last but not least, it shows the importance of accounting in the process of politicization – a dimension that has been typically neglected by political CSR scholars.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".