Dataset on the expression "Canadian model" in the Swedish Press and student papers (2021-2023)
Bibliographic record
Abstract
This dataset is part of a study exploring representations of the "Canadian model" in Swedish media and among students enrolled in Canadian Studies courses at Stockholm University. The data covers media articles, radio transcripts, and anonymized student essays collected between 2021 and 2023. It includes qualitative and quantitative content, focusing on discourse analysis of how the "Canadian model" is portrayed in social, political, and academic contexts.Swedish Media Corpus (2021-2023): This section contains articles from various Swedish newspapers as well as radio transcripts that mention the "Canadian model" or similar terms such as "kanadensisk modell" and "kanadensisk förebild." The media content was sourced from the Mediearkivet database and the National Library of Sweden. The list of articles (from Mediearkivet) is present but for copyright issues, we were not allowed to attach the whole articles. Student Papers on Canadian Studies (2021-2023): This section includes anonymized essays written by students from Stockholm University as part of an introductory course on Canadian Studies. Each essay engages with the concept of the "Canadian model," responding to a quote from Canadian Prime Minister Justin Trudeau. The student papers are available in text format (PDF/Word) and are provided with metadata that includes the year of submission and the total word count. The collection comprises a total of 155,692 words across 87 student essays. This dataset is available for academic research purposes under the terms of a Creative Commons license. All data has been anonymized in accordance with GDPR principles, and no personal identifiers are included. A course plan corresponding to the course at the basic level has also been added.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.081 |
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".