Bibliographic record
Abstract
The purpose of this study was to examine faculty responses to academic integrity violations. The study focused on the ability of Theory of Planned Behavior model to predict the target behavior of whether faculty would speak face-to-face with a student suspected of cheating. After an elicitation phase to determine modal salient beliefs, a questionnaire was developed to measure the Theory of Planned Behavior variables, respondent demographics and additional characteristics of interest related to faculty responses and opinions on academic integrity violations. The respondent database contained 206 tenured and non-tenured faculty from two large comprehensive universities, one located in United States and the other in Canada. A stepwise multiple regression demonstrated the usefulness of the Theory of Planned Behavior. Overall the model explained 43% of the variance in predicting faculty members' intention to speak face-to-face with a student suspected of cheating. The most significant contribution was made by subjective norms (β = 0.39), followed by attitude (β = 0.34), and perceived behavioral control (β = 0.24). Specific questions pertaining to demographic, cultural, gender, and experiential variables were also addressed. Among the significant findings were that the departmental chair was the most important influence as to whether faculty members dealt with cheating; that individuals with previous bad experiences were less likely to confront cheaters; and that both male and female faculty believed that male faculty have an easier time dealing with students suspected of cheating. Particular attention was paid to faculty members who ignored or failed to act on student cheating. While the majority of faculty (76.7%) felt that instructors should always deal with instances of cheating when suspected, 40.3% of respondents indicated they had ignored suspected incidents of cheating. Based upon the results, recommendations were made regarding administrative policy and support for faculty who must deal with academic integrity violations, a problem which 61.7% of faculty members reported was one of the most negative aspects of the job.
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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".