Case Study: Media Coverage of US and Canadian Elections
Bibliographic record
Abstract
When Americans think of our neighbor to the north they usually think of a near Utopian version of the United States that exists comfortably with free health services and almost no issues compared to that of the States. Canadians themselves like to think they are a better version of America as well but what happens when we dig a little deeper into say how each countries media covers its own elections? In theory Canada should not be nearly as rough and patronizing as those silly American elections one might think, but this simply isn't the case. Although not as extreme as the onslaught of media coverage that surrounded the 2016 general election in the States, Canada’s general elections are not anywhere near what Americans think of it. Canadian Media can go just as hard at the candidates as US media does. A Solid example being what is happening right now as the Prime Minister is under stress from controversy. The CBC, The Toronto Sun, and other prominent Canadian news outlets are dive bombing at every step with the first word being given to the main opposition to Trudeau in this years Canadian federal election election being Andrew Scheer, leader of the Conservative Party of Canada. Comparing the first few months of 2016 in America and the first few months of 2019 in Canada breaks the stereotype of the Utopian northern neighbor altogether and gives a glimpse into how similar but different countries media acts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".