Global Abstractions: The Classification of International Economic Data for Bibliographic and Statistical Purposes
Bibliographic record
Abstract
SUMMARY This paper compares the representation of national and international agricultural economic information in the North American Industry Classification System (NAICS) and the Library of Congress Classification (LCC). While LCC presents geographically-specific information within a larger context of agriculture as a field of study, NAICS presents agriculture as part of the overall depiction of economic activity in and between countries. To facilitate statistical aggregation and cross-comparison, NAICS has normalized economic activity by presenting it as a series of abstract activities that can be uniformly measured across different countries and regions. This rigorous standardization of economic data, while effective for statistical analysis, threatens to diminish the specific national, cultural and social contexts in which such data must be interpreted.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.146 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".