Favouring Responsible Publishing: 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
Abstract INTRODUCTION: There may be value to understanding the interests and needs of a journal’s audience, particularly regarding open access publishing (OAP) and behaviours associated with predatory publishing while establishing a new field-specific journal. As a new journal facing potential challenges in the publishing space, the Journal of Natural Health Product Research (JNHPR) undertook a stakeholder and community feedback initiative on publishing research in the field of natural health products (NHPs). To our knowledge, this is the first study where academic representatives of the journal used this method to examine the knowledge, attitudes, and opinions of its potential audience. METHODS: A database of international researchers in the NHP field was built using publicly available online data. Most NHP researchers were identified by a keyword-based, systematic search, with a small percentage discovered through snowball sampling. A survey was distributed to all identified researchers to collect their knowledge, attitudes, and opinions about OAP and the JNHPR. RESULTS: The survey was completed by 167 NHP researchers and demonstrated a wide range of attitudes and opinions about OAP. Most respondents were familiar with OAP and preferred the OAP model over a subscription-based journal. Additionally, responses indicated that OAP is a polarizing and controversial subject. Positives included the wider circulation and potential for shorter publication times, while negatives included the potential for less rigorous peer-review standards and generally higher costs. Regardless of perceptions on OAP, impact factor, reputation, scope, and indexing were the most valued factors when choosing a journal for submission. DISCUSSION: According to the survey results, the JNHPR excels in some areas while also needing to improve in others. The journal succeeds in two areas: its broad scope, which attracts NHP researchers from a variety of disciplines, and its rapid publishing time. Indexing and further reduced publication fees for lowincome nations were mentioned as areas in need of improvement.
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 | 0.067 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".