Seen from the other side of the border : Press coverage of the 2016 US presidential election campaign in Canada
Bibliographic record
Abstract
This report analyses how the press in Canada covered the 2016 US presidential election campaign. In addition, the results obtained in this work are compared with findings from a similar study which focused on three European countries, namely, Sweden, Italy and the UK. The report is based on a quantitative content analysis of four newspapers which are The Globe and mail, Toronto star, La Presse and Le Journal de Montréal. The content analysis was carried out at the DEMICOM research centre, Mid Sweden University. The findings show among others that the horse race and scandals frames largely dominated the Canadian press coverage of the 2016 US presidential election campaign. Furthermore, the results prove that the Republican party candidate, Donald Trump was given more attention in the Canadian media than his challenger from the Democratic party, Hilary Clinton. On this aspect of media attention, the results were similar in Canada, the UK, Italy and Sweden. However, contrarily to the UK and Italy, the Canadian press was more neutral in tone in its coverage of the candidates.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".