Research Note: Numerical Governance and Expertise: The FAO Before WID
Bibliographic record
Abstract
Nicholas Rose has noted the intimate connection between counting populations and them. By classifying people into categories (age, gender, education, marital status, etc.) and according to patterns (of birth, unemployment, migration, illness, etc.), the needs and deficiencies of a country and its people are confirmed, assuring particular strategies of governance (1999). Our previous work has shown that during the post-World War II development era the United Nations (UN) played a major role in creating new knowledge about agriculture, food, labour, and people in underdeveloped countries (Ilcan & Phillips 2000). Underlying this new knowledge was a penchant for producing numerical data, as indicated in the massive data banks of the U N . In this paper we look specifically at the Food and Agricultural Organization (FAO) of the U N and its early mandate to compile central registries of comparable nation-based censuses and statistics in order to discuss the implications of governing by numbers for our historical understanding of rural populations. We are particularly interested in understanding how certain modes of calculation became integral to the FAO's early concern to gauge and rurality in these contexts. It has been noted by some authors (Moser 1993, 59) that during the period after World War II and before the emergence of an explicit women and development (WID) orientation, the social welfare orientation of development was gender blind and only involved a passive role for women, if women were recognized at all in the process. However, viewed through the lens of numerical governance, one can see how and other social relations were indeed mobilized through a rearrangement of the rural in this early period. We begin by discussing the FAO's historical focus on rural social welfare. On the premise that global food shortages had reached crisis proportions, the FAO's efforts to improve rural welfare became calculable as a science that demanded refined methods to define and monitor the problems that experts were to address.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".