Responsible CSR Communications: Avoid “Washing” Your Corporate Social Responsibility (CSR) Reports and Messages
Bibliographic record
Abstract
With the rise of Corporate Social Responsibility (CSR) reporting, questions have emerged regarding its true utility; CSR reports may more closely resemble marketing materials than financial statements as much of the data companies provide can be cherry picked. For example in 2011, only 20% of S&P 500 companies published CSR reports vs 85% in 2017 and 90% in 2019. Why is this relevant for communicators? Because the responsibility of producing and promoting CSR reports very often falls under the responsibility of the corporate communications team. How to avoid "CSR-washing" and all the other “washing” incidents – green-washing, blue-washing, rainbow-washing, vegan-washing,...? How to focus on portraying the organization as a truly and authentic dedicated corporate citizen? In-depth interviews with 15 senior communication practitioners in Canada helped identify what are “authentic” and “responsible” communications in the CSR space: using facts and testimonials, being transparent, showing authenticity as well as demonstrate the clear alignment with the organization’s purpose.
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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".