EDI and open access: How JACMP is the future of ethical publishing—A tale in two parts
Bibliographic record
Abstract
At its core, scientific publishing in healthcare is meant to help society.A scientist is researching and presenting data that will influence decisions for patient care.Some data is meant to help patients in the clinic now, some data might help patients 5 or 10 years in the future.Secondarily, publishing can help the scientist, allowing the author to advance in their career and continue to research and publish.I believe it is safe to say that each Medical Physicist wants to see our scientific community improve and advances made in care for our patients.A crucial step in this improvement is ensuring that all the best minds are working toward this goal.To get the best minds in our community, we must integrate equity, diversity,and inclusion (EDI) into our scientific publishing process.EDI is a convenient acronym and easy to view as a single idea, but each component should be considered individually.Equity, as defined by the Merriam-Webster, is "justice, according to natural law or right."It is often used to describe what is "just" and "fair."It can also often be confused with equality, which is "the quality or state of being equal."Essentially, equity is fairness and equality is sameness.Equity is often used to describe the resources provided to an individual.Equitable resources could mean providing different resources for different people, depending on their needs.Equity can be a tricky concept, because it can be argued that it is not fair to provide different resources to one group and not another.This also raises the question of merit.It can be argued that resources should be provided to those that have earned them, based on their quality, not on their need.Diversity is "the condition of having or being composed of differing elements."Regarding scientific publishing, it is easy to think of diversity only in terms of race and gender.But there are a multitude of ways in which people can be different.Diversity should be considered in the data we are collecting, the authors writing the manuscripts, and the editors and reviewers who are reviewing the manuscripts.The definition of inclusion is straightforward, "the act of including: the state of being included."However, the integration of inclusion can be
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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 | 0.120 | 0.298 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.075 | 0.081 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.031 | 0.037 |
| Insufficient payload (model declined to judge) | 0.030 | 0.014 |
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".