Bibliographic record
Abstract
The British ran some excellent boarding schools on hill-stations in Northern India catering mainly to the children of British residents and first generation Anglo-Indians with an odd Harding or Halifax from the aristocracy thrown in. The very few Indians, WOGs (Worthy Oriental Gentlemen) as they were called, had to stick together. They couldn't afford a Hindu/Muslim divide.By the mid 1930s and 1940s that started to change. Day-schools began to appear. Politics from the countryside tickled in. The clamour of Inqilab zindabad (long live revolution) and Pakistan zindabad (long live Pakistan) began to filter in.K.C. Panth was the leading day-scholar rooting for the Congress. His father was the Provincial President of the Party.In the forefront from the Muslim League were the Alvis. Ata' s father Dr Illahi Alvi headed the Muslim League. He owned almost everything in Nainital including the only Indian cinema house. Indian boys from Sherwood and St. Joseph's college got free entrance to the matinee show; that is if they could sneak out after lunch.All that ended in 1947 when Dr Ilahi left for Pakistan. Most Muslim boys who had the wherewithal also left. Of the five Indians in my class (7th standard), three were Muslims. They were all gone except for myself.My family had hit hard times. My elder brother Capt. Mahmood Rizvi (Dufferin: 1929-31) died at the tail end of the World War II. My father had died a few months before that.Those days, servants were brought in from the village lock, stock and barrel with their families and would be attached for life.The most important one for me was the coachwan's (coachman's) family. His wife was my 'nanny', who had breast-fed me and raised me upto boarding school age. We had become a very large, unwieldy clan. Going to Pakistan was out of question.My parents had died. My nanny, (Anima mother to me) had also died. My only attachment was to an aging, striped grey hound. With my peers from school all gone to Pakistan, 1 felt cheated. Somehow, going to Pakistan seemed way out and it became my obsession.By 1952, I had moved to Kanpur and was taking flying lessons at the Hind Provincial Flying Club. I had also joined the Christ Church College. This was the Diamond Jubilee year of the College. A big show was organised, a sort of farewell for Rev. R.G. Slater, our principal. The two-day athletic meet was the prime event. A large trophy for the athletic champion was to be presented at the Convocation.I became the Diamond Jubilee athletic champion and was already flying aeroplanes which was enough to get to any teenager's head. Soon I was walking on air, brimming over with confidence, ready to go to Pakistan.A train from Kanpur to Monabou and on to Kohkrapar (Pakistan) was leaving soon. No travel documents were required. Alas! I had no place to go to in Pakistan and then of course there was the question of money.Mr Azhar, a pretty nosy member of the staff, found an answer to both these problems. He knew that there were sufficient funds remaining in my account at the Flying Club while I wasn't aware of this. If I could send in an application signed by Mr Tikkoo, our Chief flying Instructor, he could manage a refund. No one would know, 1 could also stay with his older brother, already in Pakistan.The catch of course was that I would have to foot the travel expenses of his younger brother Hyder and take him along too. …
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.019 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".