Rodolphe De Koninck and Jean-François Rousseau. Gambling with the Land: The Contemporary Evolution of Southeast Asian Agriculture. Singapore: NUS Press, 2012, xv+187p.
Bibliographic record
Abstract
Gambling with the Land: The Contemporary Evolution of Southeast Asian Agriculture Rodolphe De Koninck and Jean-Francois Rousseau Singapore: NUS Press, 2012, xv+187p.Gambling with the Land is one of a series of publications resulting from an international research project on Challenges of the Agrarian Transition in Southeast Asia funded by the Social Sciences and Humanities Research Council of Canada. The main aim of this book is to illustrate ongoing agricultural intensification and expansion throughout Southeast Asian countries by means of an analysis of statistical data. The book focuses in particular on rapid agricultural transformation that began in the middle of the twentieth century, and draws on statistics relating to agricultural production such as crops, livestock, land, production, yields, irrigated areas, the application of chemical fertilizers, the use of tractors, and so on. In spite of the limitations of the database examined in the book, in terms of both time and space and the quality of the data, the authors have managed to present a general analysis of agricultural data and offer a contemporary account of changes taking place in Southeast Asian agriculture.The authors identify four processes behind the agricultural transformation occurring in Southeast Asian countries: commoditization; globalization; agriculturalization; and relays and complementarities of agriculture among nations. These four processes have unevenly developed across the countries and have been influenced by various factors such as a range of national policies, a number of political events, wars, colonialism, regional and international agencies, and the ecological settings specific to the region. These processes bear out a unique feature of Southeast Asian countries: agricultural intensification and expansion go hand-in-hand, thereby contradicting the widely held belief that agricultural development intensifies only after expansion. Agriculturalization is the most interesting process discussed in the book. Research and statistical data confirm that there is an increase in labor migration as a result of the shift from agriculture to industry and services, on which the income structure has become more reliant on such domains. On the other hand, this book also demonstrates that agricultural employment in rural areas is actually increasing in Indonesia, Laos, Myanmar, and the Philippines. As such, the keyword gambling is used to describe the nature of the people who largely bet on the land.The main feature of this book is its detailed use of data derived mainly from the Food and Agriculture Organization of the United Nations (FAOSTAT) data sets between 1961 to 2007 and other figures that paint a contemporary picture of agriculture at the national level. In total, 138 figures account for a total of 187 book pages, and a large part of these are found in chapters 5 to 7 (these in fact make up the bulk of chapter 5's Agricultural Growth, Diversification, Intensification and Expansion, chapter 6's Expansion and Intensification of Food Crops and Increase in Livestock Production, and chapter 7's Expansion and Intensification of Cash Crops). Chapter 7, with 51 figures, is the most important part of this book. Statistics on cash crops such as palm oil, coffee, rubber, tea, coconut, copra oil, cocoa, and sugarcane are used to explain harvested areas, ratios, yields, productions, and the amount of exports and imports by country. …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.020 |
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".