Stata code: Additive and intersectional analyses for self-health concerns during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
This repository includes two files: (1) a secondary data file with an analytical sample, "Rahman_2022_Covid19 AnalyticalSample_StatCanadaData2020" and (2) a Stata code file, "Rahman_2022_Covid19_StataCode" . Rahman (2022) wrote Stata code to analyse a sub-sample (N=239143) of Statistics Canada’s publicly available crowdsourcing data for findings presented in a book chapter. The purpose of this chapter was to showcase the contrast between additive and intersectional approaches to examine the COVID-19 impact on intersectional groups of Canadians. See Rahman's (2022) methods section and Supplemental Figure S1 in order to learn about this analytical sample. Statistics Canada (2020a) collected crowdsourcing data online from April 3 to 23, 2020 to understand the impacts of the COVID-19 pandemic in Canada. Statistics Canada’s (2020a; 2020b) publicly available complete data set and their documentation can be downloaded from https://doi.org/10.25318/45250003-eng. REFERENCES Rahman, Laila. 2022. Concern for self-health during the COVID-19 pandemic in Canada: How to tell an intersectional story using quantitative data? In D. Woolford, D. Kotsopoulos, and B. Samuels (Eds.), Applied Data Science: Data Translators Across the Disciplines, Springer, Interdisciplinary Applied Sciences. (Accepted for publication). Statistics Canada. (2020a, June 3). Crowdsourcing: Impacts of COVID-19 on Canadians. https://doi.org/10.25318/45250003-eng Statistics Canada. (2020b). User guide for the crowdsourcing: Impacts of the COVID-19 on Canadians, public use microdata file. https://doi.org/10.25318/45250003-eng
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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 | 0.007 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.232 | 0.026 |
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".