Bibliographic record
Abstract
Getting started in SingaporeDuring my undergraduate studies at the Université de Bordeaux (1963-65), in France, I became keenly interested in Southeast Asia, although I remained primarily centred on Africa, where I had already taught as a young volunteer for a full year, in Uganda. 1 But during my subsequent MA studies at Université Laval in Québec (1965-7), my interest in Southeast Asian issues, particularly those pertaining to agriculture, became more exclusive and my professors prompted me to persist.Then, already, I did not look kindly on the fundamentally colonial relationship which characterized the system, whereby Western PhD students did fieldwork in developing countries but completed their degrees in their home countries.So, I enquired about universities in Southeast Asia and was told that the best in the region was (already) the University of Singapore.I then wrote up a research project to compare labour policies among rubber and burgeoning oil palm plantations in Johor state and applied both to the University and to the Canada Council for a scholarship.I was lucky with both applications and, as soon as I had publicly defended my MA thesis, I headed for Singapore, making several stopovers in Europe, the Middle East, India, Burma and Thailand, 2 taking six weeks to reach the City-State, where I landed on 26 May 1967.I was to remain for three years, almost to the day, those three years being essentially devoted to my PhD research and writing, as well as submitting and defending my thesis.Unfortunately, because it was difficult to get a Malaysian research permit while being based in a Singaporean University-unnecessary tensions between the new island Republic and Malaysia were already common-I had to give up the idea of doing fieldwork in Johor.So, I followed the advice to focus on what I still think was a wise choice, Singapore's own farming areas and populations.At that time, more than 20 per cent of Singapore's territory were still devoted to farming, mostly market gardening, pig and poultry rearing and some fish rearing.The so-called City State was then nearly self-sufficient in vegetables, its rural belt also supplying some 50 per cent of the local demand for pork and chicken.After consulting with several members of the local teaching staff, and not only in the Geography Department, I opted for a study of the local agricultural scene.I initially toured several of the outlying rural areasparticularly Lim Chu Kang and Chua Chu Kang and the Mandai Hills-on my newly
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".