The Majority of High-Impact Science Journals Would Accept Manuscripts Derived from Open Access Electronic Theses and Dissertations
Bibliographic record
Abstract
A Review of: Ramírez, M. L., McMillan, G., Dalton, J. T., Hanlon, A., Smith, H. S., & Kern, C. (2014). Do open access electronic theses and dissertations diminish publishing opportunities in the sciences? College & Research Libraries, 75(6), 808-821. http://dx.doi.org/10.5860/ crl.75.6.808 Abstract Objective – To assess science journal publishers’ attitudes and policies regarding open access electronic theses and dissertations (ETDs). Design – Survey questionnaire. Setting – Science journal publications. Subjects – Editorial team members from 290 high-impact science journals. Methods – The 16,455 science journals listed in the 2005-09 Thompson Reuter’s Journal Performance Indicators (JPI) were identified as the base population for this study. The top five journals, as ranked by relative impact factor, from each of the 171 JPI-defined science disciplines were selected for the sampling frame. After the removal of duplicates, defunct titles, and pretest participants, the 715 resulting journals were grouped into 14 broader subject groups defined by the researchers. Randomized systematic sample was then employed to select a final sample size of 300 journals. Ten additional titles were later removed due to publication scope. Email invitations to participate in the survey were sent to the selected journals on August 9, 2012. After two email reminders, the web survey closed on August 27. Six phone follow-ups were made to a random sample of 100 out of the 246 non-responders between September 7 and 14 to increase the response rate. Main Results – The final response rate for the survey was 24.8% (72 out of 290), and the findings had an 11.5% margin of error with 95% confidence interval. Only 12.5% of the journals surveyed indicated they would “never accept” manuscripts derived from open access ETDs, while 51.4% indicated revised EDTs are “always welcome.” The rest of the respondents had some acceptance restrictions, including case-by-case review (19.4%), accept only if the content differs significantly from the original (8.3%), accept or only if access to the original ETD was limited (1.4%). Five of the 72 respondents (6.9%) did not have a policy for accepting ETDs. Of the 17 researcher-created discipline categories, Engineering titles had the highest (85.7%, or 12 out of 14) and Medical journals had the lowest (25%, or 3 out of 14) proportion of respondents who would “always welcome” manuscripts derived from open access ETDs. At least 50% of the journals from every type of publishing entity indicated they would “always welcome” revised ETDs. However, there are differences between the entities: University Presses were most likely to “always welcome” revised ETDs (87.5%), Commercial Publishers were more likely to have some acceptance restrictions (41.7%), and Academic Societies were the most likely entity to “never welcome” revised ETDs (12.7%). Lastly, in a comparison of the results of this study with the results from a similar 2013 study conducted on social science, arts and humanities (SS&H) journals, the authors found statistically significant differences (p=0.025, α=0.05) between the editorial policies regarding revised ETDs of science and SS&H journals. Conclusion – The study results suggest that, contrary to common perceptions, the majority of high-impact science journals would actually welcome revised open access ETDs submissions. Therefore, science scholars would not greatly reduce their chances for publishing manuscripts derived from EDTs by making the original ETDs accessible online.
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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 | 0.023 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.069 |
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".