Correction: The temporal evolution of income polarization in Canada’s largest CMAs
Bibliographic record
Abstract
The authors made erroneous calculations in 10 cells of Table 2 regarding 1971 for Montreal and Winnipeg.As a result, interpolated data for 1976 was wrong and the final bootstrapped results has been affected.In addition, a data transcription error was made for Vancouver, whereby the low-and high-income data were swapped.Finally, Calgary's medium-& highincome group was adjusted by a very small amount due to a data transcriptional mistake.Please see the correct Table 2 here.To reflect the updated Table 2, the correct fifth and sixth sentence of the first paragraph of Results are: Three-quarters of the high-income group trends are significant if we consider individual income (Table 2).In general, middle and low-income groups show similar trends for both types of income, household or individual.The greatest variability is around the low and middle-income groups.The correct fifth paragraph of Discussion is: The results for household and individual income data are somewhat similar.Individual income-based data is sometimes more extreme, which suggests that the household-based income data is in some cases a more conservative means of assessing income polarization.Results for Vancouver and Quebec City tend to differ from the other CMAs.For example, no income-group trends are significant in Quebec City for individual-based income data.The converse is the situation with Vancouver, where middle-and high-income trends are not significant when household-based income data is examined.As such, there are some differences induced by different income measures.The correct sixth paragraph of Discussion is: The low-income groups for all CMAs, except for Quebec City, exhibited significant Increasing trends.Of the pairs of datasets where both types of data were statistically significant, in CMAs other than Montreal, Ottawa-Gatineau, and Winnipeg, the individual-based income data showed larger increases than the householdbased income data.
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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 | 0.008 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.108 | 0.042 |
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".