Measuring the Cultural Evolution and the Modernism of Chinese Farmers of Singapore : An Experiment in the Use of Gamma Measure and of the Guttman Scale
Bibliographic record
Abstract
In the analysis of data concerning the behavior of Chinese farmers and gathered through a sample survey of 209 households, three dominant variables kept recurring, namely the dialect group (Hokkien, Teochew, Hakka, Cantonese), the type of farm (livestock, crops, mixed), and the regional unit (8). An attempt was then made to obtain a comparative measure of the degrees of relationship that might exist between each of these dominant attributes and other variables. However the existence of a high degree of locational association between dialect groups and farm types made it particularly difficult to draw out a discriminatory measure. Furthermore, reliance on various measures of correlation was also hindered by the presence of nominal and ordinal variables, by the large number of frequencies inferior to 5, which limits the use of chi-square, etc. Finally, an adapted version of the gamma measure made it possible to distinguish comparative relations between the 3 major attributes and other socio economic characteristics. This methodological result is particularly interesting for it points to a fundamental conclusion. While in the past the dialect group had probably been the original form of group identity, it was now being succeeded by more local forms, forms more specific to Singapore, the professionnal group and mostly the belonging to a given region. In other words, in a Chinese community of the Nanyang, presumabiy in Singapore one of the most susceptible to retain strong ties with its cultural heritage, Singaporean attributes and even regional Singaporean attributes appear to be the most reliable socio-economic indicator. This essential statement is further refined by the use of a Guttman scale which serves to measure the modernism of the farming households. According to scorings on the scale, as might be expected, livestock keepers appear to be the most modem farmers ; however crop growers seem to be more modem than the mixed farmers. But what is even more important in the increasingly planned Singapore context, the farms of the part farming households seem to be just as modem as those of the households whose livelihood depends exclusively on farming.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".