CSR accounting ‘new wave’ researchers: ‘step up to the plate’… or ‘stay out of the game’
Bibliographic record
Abstract
Recent discussions at accounting conferences and workshops suggest that academics are ‘deeply divided’ on the role and purpose of corporate social responsibility (CSR) accounting. This ‘rift’ has been created by moves from mainstream accounting researchers to contribute to a body of evidence that is almost 50 years old without—many believe—being cognizant, or even respectful, of the work that has gone before. The existing work by CSR accounting scholars puts sustainability of the planet at its core, rejecting narrow or instrumental approaches to the fundamental issues; in contrast, more recent ‘capital market-based’ work takes investor-centric, or market-driven approaches to ‘sustainability’ and CSR. While there are calls for greater understanding of, and empathy for, each other’s views and perspectives, this essay identifies some particular pain-points, and calls for new wave researchers—those who recently ‘(re)discovered’ CSR accounting research—to ‘step up (to their plate)’ or simply ‘stay in their own lane (or, out of the game)’.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".