Bibliographic record
Abstract
On behalf of the Editorial Board SABITA is the online journal for Humanities being launched by the Asutosh College. The name symbolises truth, wisdom and the creative principle. This e-journal will endeavour to incorporate peer-reviewed, original scholarly research articles on varied areas of Humanities, including language studies and literature, social sciences, management studies, and even a few conventional science subjects like Economics, Geography, and Psychology, especially as these disciplines are often classified under the umbrella of Humanities. This inaugural issue is a combination of contributory articles by senior scholars and peer-reviewed articles by young researchers on diverse topics related to Indian politics, jurisprudence, social media, mental health, environmental Humanities, aesthetics, the World Wars, periodical literature, and so on. SABITA has been prepared for online publication by the coordinated efforts of the Co-Editors- in-chief, Dr. Chandramalli Sengupta of the department of Bengali, Dr. Sraboni Roy of the department of English, Dr. Rina Kar Dutta of the department of Philosophy, Dr. Subhasri Ghosh of the department of History and Dr. Supriyo Das of the department of Business Administration, working in tandem with the technical support team whose contribution should not remain unacknowledged. The Editorial Board thanks all the contributors for their scholarly articles, all esteemed members of the Advisory Board for their valuable advice, and all peer reviewers/ subject editors for finding time to evaluate the submissions. SABITA, keeping true to the connotation of the name, hopes to publish annually, scholarly articles on diverse areas of Humanities, thereby creating a platform for wide-ranging research in that field. It represents a sincere attempt on our part to foster engagement with, and nurture research in those branches of knowledge that directly relate to man as a sentient being, as a social, cultural, and political entity and in this perhaps ambitious enterprise, we look forward to the enthusiastic participation of scholars and academicians, both emerging and already well- known, who are spread across the country and beyond. Dr. Manas Kabi Editor-in-ChiefSABITA – A Journal of Humanities &PrincipalAsutosh College, Kolkata
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.203 | 0.175 |
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".