Exploring New Ways of Communicating CSR to the Relevant Stakeholders: An Empirical Study
Bibliographic record
Abstract
A flower must be pollinated properly to produce seeds as final outcome of next germination to create new flowers and so on. Accordingly Corporate Social Responsibility (CSR) actions and practices of an enterprise should be communicated with its stakeholders. CSR and CSR communication should go hand-by-hand. The recent business trend shows that businesses are becoming increasingly aware of CSR and CSR communication systems. Existing studies, firm’s case study and real world phenomenon also reveal that business houses and society are benefiting from CSR actions and communicating those with their stakeholders. This research explores CSR and CSR communicating strategies and finds that stakeholders have in-depth concerns about CSR. It’s interesting that popular CSR practices like charity and philanthropic actions have been replaced by environmental (carbon footprint, air and water pollution), legal (complying regulatory imperatives) and ethical (promoting corporate ethics, norms and values) etc. Moreover, the study also shows that communicating CSR actions through CSR reporting, company annual reports or firm’s sustainability reports and advertising have become less fashionable means of exchanging CSR efforts while academic books (companies are cited as examples or extracted as referred case studies), newspaper, internet and third party (social, political, local government authorities) association have become more trusted ways of communicating CSR motives, practices and actions.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".