Administrative data linkage in Canada: Implications for sociological research
Bibliographic record
Abstract
This paper explores some of the implications that administrative data, defined as data initially collected for purposes other than research, will have for Sociology. Although administrative data are "found" rather than "made" and, in turn, pose several challenges, we argue that the potential of these data warrant the investment, and may lead to a new methodological imagination that can shed a light on time-tested concepts and advance our understanding of society. We show that it is already possible to advance several sociological debates through the use of administrative data and demonstrate the potential of these data through some examples drawn from classical sociological theory. We conclude by arguing that administrative data's potential will likely ensure that it becomes an important component of sociological research agendas in the coming years.
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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 | 0.122 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.081 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.032 | 0.013 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".