Unethical Author Attribution
Bibliographic record
Abstract
I am an M.D/Ph.D. student and work as a research assistant for the director of a division of the school of medicine who is an M.D. He assigned me to research a certain topic and gave me no guidelines or guidance as to how to do it. Nevertheless, I did the research and wrote it up. My supervisor liked the report and said that he thought it was so good that “I would like to offer you the opportunity to publish it and list you as the primary author.” Some bells went off when he so grandly offered to let me author the report for which I had done 100% of research and writing. I consulted some other people in the field and they said that, as long as I was the primary author, it was legitimate for him to list himself as secondary author if he did some editing later. After editing the abstract only, he e-mailed his revisions to me and in a note at the bottom he asked me what I thought of his revised author order. His name was first, mine second, and the name of his girlfriend (who had no part in this research or its revision) was third. I was shocked by what seemed to be a case of unethical author attribution and confronted him asking why he changed the order when we had agreed that I was primary author. He said that he had put in several hours of work.
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 | 0.176 | 0.576 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.030 | 0.034 |
| Insufficient payload (model declined to judge) | 0.031 | 0.031 |
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".