Bibliographic record
Abstract
The public benefits of higher education have long been recognized. Higher education contributes to the public consensus; it transmits knowledge and attitudes toward the role of the citizen; and it may model good behavior in the face of controversy and sometimes intellectual acrimony. Great universities, perform these functions very well. This is among the reasons why attention has been paid to the characteristics of world class universities [1–3] as well as to the threats to university quality in the form of corruption in higher education. Attention has focused on the definition of corruption, the degree to which corruption occurs, and its economic impact [4–7]. This paper combines these lines of scholarship and explores the degree to which world class universities exhibit ethical qualities. The study defines ‘ethics’ in the management of a university. This includes mission statements which mention ethical issues, transparency in governance and fiscal affairs, codes of conduct for faculty, administrators and students, procedures for adjudication of infractions, and other elements. It then proposes a rating for the ethical infrastructure elements. Universities have been divided into two groups. First are universities listed on the Times Higher Education Supplement (THES) international ranking. The second are random samples of universities in countries which use English, Korean, Japanese, Georgian, Chinese, and Russian languages as the medium of instruction. The paper poses three questions. First, how common is it for internationally-ranked universities to exhibit ethical char-acteristics on their websites? The answer is unambiguous: 98 % of the world class universities have established an ethical infrastructure of some kind. Second, which areas of the world are more likely to have universities which exhibit a depth of ethical infrastructure elements on their websites? In terms of countries, the most comprehensive ethics infrastructure can be found in Britain, the U.S., and Japan. Lastly, what is the relationship between the level of international ranking and the depth of ethical ingredients? The strength of the relationship is weak, suggesting that the depth of ethnical infrastructure is not an important determinant of ranking. However given the fact that virtually all ranked THES universities, across 40 counties, mentioned ethical infrastructure elements, suggests that having an ethical infrastructure is an important ingredient associated with other elements in a university’s reputation.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".