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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".