0 Passing Camels through the Eye of a Needle: The Effort to Create Internationally Comparable Social-Science-Based Longitudinal Data Sets in Canada
Bibliographic record
Abstract
We thank Gert Wagner for careful readings of earlier drafts of this paper and Reverend Michael Mahler for his biblical advice. Partial funding for the research on which this paper is based came from the United States National Institute on Aging. The findings and conclusions expressed here are solely those of the authors and do not represent the views of the NIA. “It is easier to pass a camel through the eye of a needle than for a rich man to enter the kingdom of heaven ” (Matthew 19:24). As a kid growing up in a lower-middle-class Roman Catholic household, I always enjoyed watching my more prosperous fellow congregants squirm during the annual sermon based on this passage from Matthew. But my enjoyment has diminished over the years as my own fortunes have improved and the odds of me passing this test have concomitantly fallen. Luckily, a recent talk with a biblical scholar has caused me to be more optimistic about my eternal reward. When this biblical passage was written, camels were the major means of transportation and the eye of the needle was the narrow passage between the
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".