A new tool for neighbourhood change research: The Canadian Longitudinal Census Tract Database, 1971–2016
Bibliographic record
Abstract
Abstract Performing longitudinal analysis of socio‐economic change in small‐area spatial units such as census tracts presents several methodological complications and requires significant data preparation. Unit boundaries are revised each census year because of changes in population and delineation methodologies. This limits cross‐year comparison since data are not representative of the same spatial units. To address these problems, we have developed an innovative procedure to reduce error when comparing tract‐level data across census years by apportioning data to the same areal units. This paper describes the methods used to create the Canadian Longitudinal Tract Database. Our procedure is a combination of map‐matching techniques, dasymetric overlays, and population‐weighted areal interpolation. The output is a set of tables with apportionment weights pertaining to pairs of unique boundary identifiers across census years, which can be linked with census data or other data with census identifiers that require longitudinal comparison.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".