Motivations for Issuing Standalone CSR Reports: a Survey of Canadian Firms
Bibliographic record
Abstract
There is a debate in the literature regarding the underlying motivations for companies who voluntarily issuing standalone CSR reports (Clarkson et al., 2011). To gain insight into this debate we report the results of a survey which asks 221 Canadian firms their motivations, their costs and whether they follow independent GRI guidelines in choosing to issue (or not issue) their CSR report. Of the 57 companies that responded to our survey, 32 issued standalone reports and 25 choose not to. The most frequently cited reason for issuing standalone CSR reports was to signal their interest in social responsibility to stakeholders followed closely by CEO/Board commitment to CSR. Other frequent responses were that companies wished to communicate to stakeholders a policy of corporate transparency and to put all CSR information in one place. Approximately 71% of companies that issue CSR reports follow GRI reporting guidelines and 25.8% of these CSR Reports are independently verified. Over 50% of companies reported that it costs over $75,000 to produce the standalone CSR report, takes over four months to prepare on average, and requires five or more people to produce it. The primary reason that companies chose not to issue CSR reports was lack of stakeholder pressure and regulatory requirement to do so. Our research provides support for the need for multiple perspectives to provide insight into standalone CSR reporting.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".