Hanging in the Balance: Using Technology Without Losing Touch in the Canadian Census of Agriculture Claire Bradshaw, Processing, Census of Agriculture
Bibliographic record
Abstract
Abstract: In recent censuses, the Canadian Census of Agriculture has adopted new technologies in data collection and processing. While these technological innovations have made collecting and processing the agricultural census faster and have provided many tools to ensure data quality, they carry a price. The push to more cost-effective collection methodologies has coincided with increased demands for privacy and confidentiality; in combination, the two have decreased the quantity and quality of personal contact with respondents. Each five-year census presents new challenges in ensuring that the agriculture census addresses the important issues of a very rapidly changing agriculture industry. The collection process, although still largely paper-based, has incorporated Internet and computer-assisted telephone interviewing, limiting opportunities for ad hoc feedback and face-to-face contact. This paper presents the issues and solutions for balancing respondents ’ privacy and confidentiality, and the growing adoption of more impersonal and fragmented data collection options.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.014 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".