Cost in high impact journals: The problem for researchers from low and middle income countries
Bibliographic record
Abstract
Introduction: Research publication is an important means of career progression for young professionals. At many universities, it is a mandatory requirement to publish a certain number of research paper for raise, promotion, or even recruitment. Today wider acceptance of the open access model has increasingly shifted the cost of publication from the reader to the researcher. The present study aims to study the processing and publication, pricing of index journals. Method: This was anobservational study, studying journals indexed in one of the freely indexing services. We retrieved list of all journals indexed in PubMed between June and December 2015 from the website. We non randomly selected 150 of these for further evaluation study. The outcome measure was publication cost, and pre-processing cost. We retrieved cost related to publication and publication site of the journal from the official website of these selected journals. For cost estimation, a typical article length of 15 pages was assumed. We excluded journals whose websites did not provide information regarding such cost or were in any language other than English. Results: Out of 5623 indexed journals in PubMed as of July 2015, 150 journals were included in the study. A total of 33 journals was excluded, remaining 117 journals formed the final sample size of this study. Nearly 80% of the journals had publication cost. The median cost of publication of a typical paper was 2590 US$. More than three quarter of the journals were published from the developed world like United States of America, United Kingdom and European region. Conclusion: Though the cost of publication was not uniformly high, the average cost was relatively high for researchers in low and middle-incomecountries.It could be as much as one-month’s salary of these professionals. This could be a hindrance for publishing research and in the career advancement of these researchers
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchScholarly communication Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Scholarly communicationMetaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.053 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".