Linkable administrative files: Family information and existing data
Bibliographic record
Abstract
Linkable administrative data have facilitated research incorporating files from various government departments. Examples from Canada, Australia, and the United Kingdom highlight the possibilities for improving such work. After expanding on comparisons of linkable administrative data with several famous studies, we forward suggestions on improving research design and expanding use of family data. Certain characteristics of administrative data: large numbers of cases, many variables for each individual, and information on parents and their children, provide building blocks for implementing these proposals. Traditional longitudinal epidemiological approaches can be modified to facilitate a quasi-experimental perspective. Incorporating multiple research designs within the same project handles threats to validity more easily. Family data provide a number of opportunities for both same-generation and intergenerational research. Risk factors associated with a number of conditions can be studied. Bad events can affect all family members, and cross-sectoral information can extend analyses beyond health to include educational outcomes. Parent/child linkages suggest several lines of research exploring within-family relationships. Complicated data call for family identification systems to estimate project practicality. Manitoba administrative data are presented to illustrate one such system. Problems in maintaining core data element – such as marital status – have been highlighted. The productivity and potential of cross-sectional, longitudinal, and life course research using existing information have emphasised the value of investments in such work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.029 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.194 | 0.048 |
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".