The French Covid-19 vaccination policy did not solve vaccination inequities a nationwide study on 64.5 million people
Bibliographic record
Abstract
Data and code to reproduce the analysis of our article. # Contents `code/`: Analysis code. The main analysis file is `vaccination-indicators.Rmd`. Some results are exported in `out*.RData` files. `data/`: Data used for the analysis. The data are treated by the `code/0_INSEE_predictors.R` script, and saved as `code/data_indicators.RData`, which is the file used for analysis. `ms/`: Manuscript files; they are outdated (the ms was later modified with Word), but `ms.Rmd` contains code to reproduce the figures and some numerical values given in the text. # Data Sources - Vaccination data from Assurance Maladie: - EPCI: - Paris, Marseille, Lyon: - Geographic information: - EPCI: - Paris, Marseille, Lyon: - Socio-economic indicators from INSEE: - 2017 Presidential election: - https://www.data.gouv.fr/fr/datasets/election-presidentielle-des-23-avril-et-7-mai-2017-resultats-du-2eme-tour-2/ - Paris: - Marseille: - Lyon:
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".