Open Access Journals: A Boon or Bane for Early Career Researchers in India
Bibliographic record
Abstract
Background : It is demonstrated that for a junior research faculty in India, proper guidelines and funding resources are required to publish research articles in the Open Access (OA) journals. Recently, many of the important scientific journals are turned out to be OA journals. When we need to publish in an OA journal, the funding support for article processing charge (APC) is uncertain due to the limited funds or absence of institutional level support. Objective: To find out the total number of open access and subscription based articles published from the top ten countries in the scientific journals up to July 2020. Materials and Methods: For the data collection, a keyword of “Chemistry” was used in all fields in the “Scopus database” on 07 July 2020. Results: From the articles published by top ten countries, it was found that USA has published more number of publications (open access plus subscription based) followed by China, Japan, Germany, UK, India, France, Canada, Italy, and the Russian Federation. However, if we carefully look at the total numbers of OA publications up to July 2020, Japan (33.47%) has published more percentage of OA documents compared to UK (26.92%), Germany (24.63%) and the USA (24.53%). In this list, India (13.02%) and the Russian Federation (10.14%) have published the lowest numbers of OA publications compared to other countries. Conclusion: It was found that about ~50% of OA publications resulted from India might come from collaborative research. The APC may be supported by other countries along with India. In addition, it was obvious that the Indian Institute of Science (IISC) had published the highest OA papers, followed by CSIR India and the University of Delhi. From the past ten years, OA publications from India were doubled in number from 2011 to July 2020. However, it requires further efforts to increase our scientific progress and research accomplishments by the number of publications, patents, and commercial products to support the Make in India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".