Mapping the Literature on Corporate Sustainability and Public Policies
Bibliographic record
Abstract
This paper aims to overview the main topics discussed in the academic papers related to public policies and corporate sustainability. Based on the literature, our scope is to set up a framework for future research on public policies and corporate sustainability by constructing bibliometric networks. The study will evidence the authors with some of the most important research in this field, based on the citations of their papers, and the countries with the highest number of publications and citations. Through the VOSviewer software we studied the topic of corporate sustainability and public policy based on keywords, citations, geographical distribution, and authorship. After observing 289 papers indexed in Web of Science, we found as frequent keywords corporate social responsibility, sustainable development, performance, management, legitimacy, or disclosure. The highest number of papers were published in American Journals, but also in England, Australia and Canada, but the most cited papers were the American and Canadian ones. Finally, T. Sueyoshi and M. E. Porter are the most cited authors for papers on corporate sustainability and public policy.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.074 | 0.120 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".