Corporate social responsibility disclosure: a topic-based approach
Bibliographic record
Abstract
In this study, we investigate the potential differences in topic-specific corporate social responsibility (CSR) disclosure between companies located in liberal market economies (LMEs) and coordinated market economies (CMEs). We also examine the potential convergence of the reporting practices that characterise these two economies over time. We analyse a sample of 5,939 CSR reports issued by European and U.S. firms over 2008–2019. We use textual analysis to examine how explicitly such reports address specific CSR topics. Following Matten and Moon (2008), we focus on three thematic areas: ‘human resources’, ‘environmental protection’, and ‘society at large’. Each area comprises three distinct topics. Our results show that companies operating in LMEs report more explicitly on these thematic areas, with one exception: those operating in CMEs report more explicitly on parental policies. Additionally, the reporting practices of companies operating in these two types of economies converge for most of the topics under study. For the disclosure of parental leave policies, biodiversity, and waste, no distinct trend is observable.
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.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".