Bibliographic record
Abstract
Introduction Researchers have a moral and social contract to uphold several ethical principles in the conduct of research. Much of scientific, social science, and humanities research in many nations is paid for by society through public funds.' Hence, researchers have a social responsibility to ensure that their conduct of research is performed with the highest standards of professionalism, ethics and integrity. The responsible conduct of research (also known as scientific or research integrity (1)) includes a set of norms and practices that applies to researchers in any discipline. Several principles underlie the responsible conduct of research (RCR), such as, honesty, carefulness, openness, fair credit, respect for colleagues, respect for human and non-human animal subjects, legality, education, and social responsibility. (2) These principles guide all aspects of RCR including research design, the collection, analysis and dissemination of results, the ethical treatment of human and animal subjects, providing appropriate credit to colleagues and students, being open to criticism and review, sharing data, reagents and methods, and avoiding conflicts of interest. A violation of these practices can lead to different harms to the researcher herself, other researchers, research subjects, or society. This can be in the form of research fraud, undermining the health and safety of research participants, preventing scientists from replicating results, or wasting resources. However, research is performed by human beings and human frailties inevitably appear, sometimes in the form of research misconduct. (3) Much of the Canadian academic literature focuses on the ethics and governance of research involving humans, animals, and conflicts of interests. Little attention, however, has been paid to the ethics and governance of RCR. This paper aims to provide the scope of research misconduct cases reported as news in academic journals and the Canadian popular press, and review the RCR practices and policies in Canada, including various relatively recent initiatives conducted by different governmental and non-governmental organizations with the goal of strengthening the Canadian research integrity system. Research Misbehaviours in Canadian Institutions Awareness of RCR by scientists, bioethicists, the media, governing organizations, and the public has been heightened by widely publicized scandals of research misconduct. Many international RCR policies arose from major scandals hitting nationwide headlines and Canadian research misconduct cases have also been featured in the news. In a large multi-centre breast cancer trial, Dr. Roger Poisson recruited patients who didn't fit the inclusion criteria claiming that he couldn't deny women the best available treatment because of a criterion that had little or no oncological importance. (4) This led to an investigation of several of his studies and in 1993, Poisson was convicted of research misconduct in the U.S. where 115 documented instances of fabrication and falsification were found. (5) Dr. Poisson, a member of the medical faculty at the University of Montreal, was forced to retire a month earlier due to these findings. (6) A second case that received considerable media attention, including an expose by CBC News, was with Ranjit K. Chandra--a retired professor at Memorial University of Newfoundland who was accused of research misconduct in several studies. (7) A third case involved plant researcher Fawzi Razem, who worked in a laboratory of a professor at the University of Manitoba and was found to have fabricated data. (8) Resigning from the University of Manitoba after the initial allegation was made, Razem later turned up to be working as faculty at the Palestine Polytechnic University. (9) Another highly publicized case involved Dr. Eric Poehlman who was hired by the University of Montreal in 2001 while he was being investigated for fabricating research at the University of Vermont College of Medicine and previously at the University of Maryland. …
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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 | 0.071 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.029 | 0.029 |
| Scholarly communication | 0.033 | 0.007 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".