Bibliographic record
Abstract
All Thanks to Almighty Allah Who blessed me with the ability to do this work and Hazrat Muhammad (Peace be upon him) who gave us the spirit to learn. It is the blessings of Allah and His Prophet (Peace be upon him) that have enabled me to make my e¤orts a success. During the last few years of my Ph. D. studies, I have interacted with so many people who have greatly facilitated my academic career; I am greatly indebted to all of them since with their sincere and utmost support I have been able to make my aspirations a reality. A teacher undoubtedly plays an important role in the life of a student. I am greatly thankful to my principle supervisor Prof. Dr. Nazir Ahmad Mir for granting me sincerest and supreme guidance at every stage which evoked in me an insight to carry out my research work e ¢ ciently. Without his a¤ectionate, generous and distinguished supervision during the research work and in compilation of this dis-sertation, I would not have been able to complete this work. I am highly obliged to my second supervisor Dr. Nusrat Yasmin for her valu-
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.022 |
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".