Publication among academic staff and students: an analysis from the ethical perspective
Bibliographic record
Abstract
This article analyzes, from the ethical perspective, the authorshipof particles carried out among students and professors andtheir potential conflicts. After the literature review, it has beenfound that the Vancouver criteria that should be fulfilled for theattribution of authorship of an article are not popularly known bystudents and academic staff. Many problems are posed in thisarea, among which the following are highlighted: ghost writer,honorary author, and incorrect assignment in the order authorsshould appear. The professor-student relationship brings withit implicit risks that could lead to conflict, against which it isthe academician who should be cautious to curtail any ethicalfault when assigning the authors. The measures recommendedto avoid conflicts of authorship among students and academicstaff are: early assignment of the authors, reflection amongacademicians, education to students/academic staff, and externalcontrol conducted by journal editors. Conclusion is that lack ofawareness of the criteria of authorship by academicians andstudents is the principal problem in the attribution of authorships.It is indispensable to improve this knowledge and look after theapplication of said criteria in practice.
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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 | 0.041 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".