Bibliographic record
Abstract
Ethics in higher education is the subject of intense public attention, with considerable focus on faculty roles and responsibilities. Media reports and scholarly research have documented egregious misconduct that includes plagiarism, falsification of data, illicit teacher-student relationships, and grading bias. These accounts of wrongdoing often portray faculty ethicality as only a legal issue of obeying rules and regulations, especially in the teaching and research roles. My discussion challenges this narrow perspective and argues that characterizations of faculty ethicality should take into account broader expectations for professionalism such as collegiality, respect, and freedom of inquiry. First, I review the general principles of faculty ethics developed by the American Association of University Professors, as well as professional codes of ethics in specific professional fields. Second, I juxtapose the experiences of women and minority faculty members in relation to these general codes of ethics. This section examines three issues that particularly affect women and minority faculty experiences of ethicality: "chilly and alienating" academic climates, "cultural taxation" of minority identity, and the snare of conventional reward systems. Third, I suggest practical strategies to reconcile faculty practice with codes of ethics. My challenge is to the faculty as a community of practice to engage professional ethics as social and political events, not just legal and moral failures.
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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".