Favouring Responsible Publishing: A Protocol for Creating a Database of Researchers and Surveying their Knowledge, Attitudes and Opinions towards Open Access Publishing and a New Field-Specific Journal
Bibliographic record
Abstract
Background: As a new publication, the Journal of Natural Health Product Research (JNHPR) sought to develop a two-step protocol that (1) creates an international database of natural health product (NHP) researchers and (2) surveys their perceptions, needs, and preferences for a legitimate, broad-based, open access scientific journal in their field. This protocol could be useful to other researchers and publishers and can be adapted to establish similarly legitimate journals within their academic discipline.Methods: The database will initially be created using publicly available information online. A keyword-based, systematized search will identify the majority of NHP researchers, with referrals completing the process. Key research and publication characteristics of each person will be recorded. Subsequently, a survey will be developed and administered to all identified individuals to capture their attitudes and opinions on both open access in general, as well as the JNHPR. Construction of our NHP researcher database began in early-2018 and the development of our survey began in mid-2019. The survey is slated to be administered in early 2020.Conclusion: This protocol provides a process for constructing an international researcher database and a survey that aims to understand the community of NHP researchers’ interests and needs regarding open access publishing and a new field-specific journal. This process can be modified and replicated for the purpose of establishing other legitimate journals in any scientific research discipline.
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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 | 0.214 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.101 | 0.048 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".