Bibliographic record
Abstract
Dear Readers, It is our immense pleasure to announce that the Journal of Interdisciplinary Sciences (JIS) is now indexed in the DOAJ: Directory of Open Access Journals. JIS is also indexed in JUFO: Julkaisufoorumi and NSD- Norwegian Register for Scientific Journals, Series and Publishers. As we commit ourselves to serving the academic and scientific communities, the purpose of serving has deepened even more for “Enriching Beautiful Minds towards Intelligent Minds” in a greater meaning. As we have struggled to reach this far since 2017, we are now even determined to walk extra miles. This encouragement comes from the authors and reviewers who have trusted in our confidence, intelligence, and fairness. In this volume 9, issue 1 of May 2025, we have published scientific papers from various disciplines and different countries. These published papers are openly accessible to all readers and uploaded to DOAJ and other indexing platforms. Submission to JIS is welcome from authors from all subjects. We look forward to more participation to Enrich the Beautiful Minds towards Intelligent Minds. Thank you very much Let us join and collaborate together to ENRICH the BEAUTIFUL MINDS TOWARDS INTELLIGENT MINDS Mani Man Singh Rajbhandari. Ph.D. Founder and Editor-in-Chief Journal of Interdisciplinary Sciences (JIS) www.journalofinterdisciplinarysciences.com
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.098 | 0.070 |
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".