Corporate governance and business ethics
Bibliographic record
Abstract
T he primary objective of a corporation is to increase shareholder value.Successful corporations must operate within society; to that end, they must maintain the values and norms of the society in which they operate.Volkswagen has been the unfortunate recipient of a great deal of press time lately.In case you missed the details, it recently came to light that Volkswagen knowingly deceived the United States Environmental Protection Agency (EPA) with respect to nitrous oxide (NOx) engine emission for their TDI engines.The company programmed the vehicles to favourably behave differently during EPA testing.The engines actually exceeded emission test levels during every day use by roughly 40 fold.The number of affected vehicles is not small -approximately 11 million cars worldwide.While the old adage goes that there is no such thing as bad publicity, the company's publically traded market share losses topped €14 billion during the fallout, suggesting otherwise.The scandal has fueled the ire of those who question the altruism and decry the intent of big business.The scandal has thrown the subject of business ethics back into the spotlight.The corporations or organizations surgeons typically navigate are hospitals and universities -institutions held to strong social standards of ethical accountability.However, hospitals are not the only organizations we interact with.We use the products of for-profit corporations and make decisions on behalf of our patients many times without them knowing a choice has been made.We have the good fortune of working with ethically strong corporate partners in a highly regulated industry, but Volkswagen has shown us it is both academically interesting and prudent in practice to understand the ethical tenets governing our corporate partners in patient treatment as they balance their efforts to advance medical research while increasing shareholder value.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".