Bibliographic record
Abstract
Cheating and academic dishonesty is a moral anomaly in the field of scientific research and reflecting, i.e., academic environment and studies show that this phenomenon in many of the worlds is important problem. This study measured the dishonesty of students in a quasi-experimental design. For this purpose, features lack of integrity by manipulating the facts were examined and meanwhile first, basic English language test coordination between the strict terms of the 280 students come to practice and after correction of examination papers by teachers, without leaving any traces on them instead, the plates are returned to students and provide them with answers to their paper to correct their score Master announced. The difference between the actual score (score of master) and score of the students to have their own, amount of honesty or lack of integrity appointed them and its relationship with some demographic and socio-ethical characteristics have been studied. The results showed that more than 62 percent of the students in your grade to master completely honest with 26.6 percent have low honesty and the rest did not have the necessary integrity and the mean difference of scores announced by the professors and students have been about two score. Also results of chi-square tests and gamma, about the relationship between students’ evaluation of amount of sincerity with sincerity in the declared objective amount of the master score was not significant, this finding means that between demonstrators and people of integrity and honesty in practice, there are gaps.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".