Proceedings of the Survey Methods Section PUSHING THE LIMITS: USING STATISTICS WITH VARYING AMOUNTS OF EXPERTISE
Bibliographic record
Abstract
Statistical adequacy can be judged according to two different sets of criteria: those proposed by mathematical statisticians and those preferred on grounds of practicality by researchers. Mathematical statisticians have been particularly concerned to improve estimates of standard errors. We argue that this concern may be exaggerated. First, problems caused by missing values and over-fitting of models are probably more important sources of error in social research. Second, much social research using surveys often involves the search for patterns across many different analyses and specifications. Procedures that are technically difficult to apply or that require complicated judgments are likely to hinder this process. We illustrate this argument with the recent history of Statistics Canada’s recent recommendations on how to deal with complex design effects.
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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 | 0.414 | 0.593 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".