Bibliographic record
Abstract
I had known Professor Abdul Hamid (d. 1980) for over two decades, during the last part of his life. I had first heard of him way back in 1953 when I was a student at the Institute of Islamic Studies at McGill (Montreal). He had just then done his Ph.D. thesis on Sir Syed Ahmad Kahn. Prof. Smith, Wilfred Cantwell, the Institute‟s Director, told me that he was due to visit the Institute for a few days at the end of his (first) stint in the U.S.; but, then, for some reason or other he did not. However, since 1963 when the Pakistan History Conference was hosted by the Punjab University, I had met him off and on whenever he visited Karachi and I Lahore, and at seminars and conferences. I had also corresponded with him on various academic matters, and after my appointment as the first Director of the Quaid-i-Azam Academy in January 1976. I had had extended consultations with him about the projects that had to be set up, the areas that called for early attention, and the steps that ought to be taken into making the Academy, then only a paper body, into a leading research institute. He also did a mini volume for the Quaid‟s Centenary series, entitled On Understanding Quaid-i-Azam, which was published by the National Book Foundation (NBF) on behalf of the Centenary Committee whose publication programme I was looking after
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.040 | 0.037 |
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".