Bibliographic record
Abstract
Purpose – The purpose of this study is to investigate the degree to which business schools, in particular MBA programs, have developed academic programs and centers specifically focused on corporate social responsibility and sustainability (CSRS) and, for those that have, promote them on their Web sites. The instruction of CSRS in institutions of higher education is increasing worldwide. The extent to which US MBA programs have developed academic programs and centers focused on CSRS could potentially be a way for business schools to distinguish themselves from other schools. Design/methodology/approach – The authors use a Web-based search of the Web sites of the top-100 US MBA programs to ascertain the extent to which they have developed CSRS-related academic programs and centers. They then look specifically at the full-time MBA main Web page to ascertain to what extent these programs promote CSRS material. Findings – The results suggest that schools in the top quarter and bottom quarter, as well as private schools, are more likely to have CSRS academic programs and centers. The authors also find that very few full-time MBA programs promote CSRS on their main MBA Web pages. Originality/value – This study is unique in its focus on the top-100 US MBA programs and the collection of primary data directly from their Web sites. Additionally, a summary of the data gathered from the MBA programs is provided in Table I of the study.
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 | 0.009 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.062 | 0.047 |
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".