Evidence for the use of an algorithm in resolving inconsistent and missing Indigenous status in administrative data collections
Bibliographic record
Abstract
Measures of the gap in living standards, life expectancy, education, health and employment between Indigenous and non‐Indigenous Australians are primarily derived from administrative data sources. However, Indigenous identification in these data sources is affected by administrative practices, missing data, inconsistency, and error. As these factors have changed over time, assessing whether the gap between Indigenous and non‐Indigenous Australians has changed over time, based on data unadjusted for these sources of error can potentially lead to misguided conclusions. Combining administrative data on the same individuals collected from different sources provides a method by which a more consistent derived Indigenous status can be applied across all records for an individual within a linked data environment. We used the Western Australian Data Linkage system to produce derived Indigenous statuses for individuals using a range of algorithms. We found that these algorithms reduced the amount of missing data and improved within‐individual consistency. Based on these findings, we recommend our Multi‐Stage Median algorithm be used as the standard indicator of Indigenous status for any reporting based on administrative datasets when multiple datasets are available for linkage, and that algorithmic approaches also be considered for improving the quality of other demographic variables from administrative data sources.
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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 | 0.337 | 0.593 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".