Bibliographic record
Abstract
This thesis reports on a rhetorical study of endorsement editorials published in Canadian newspapers during the spring 2011 federal election. These editorials, intended to encourage readers to support or vote for a candidate or party, draw their persuasive power from a combination of rhetorical genres and appeals. \nAn endorsement editorial expresses a newspaper’s backing of a party or candidate; it may also urge readers to support and vote in a similar manner. Endorsement editorials are a significant area of study because they are usually published only during election campaigns and respond to a perceived exigence or need to address pressing issues. My sample set includes editorials published in English-language daily newspapers: the Globe and Mail, National Post, Toronto Star, and Toronto Sun. The Toronto Star published two endorsement editorials, one of which I describe as a “dis-endorsement” to reflect the message to not support a specific political party. \nThis study examines three elements I deem important for successful argumentation: arrangement, argumentative strategy, and audience. I identify seven elements of an endorsement editorial: thesis, endorsement, call-to-action, kairos or time-to-act, evidence, refutation, and context. My study draws on the theoretical frameworks of Chaim Perelman and Lucie Olbrechts-Tyteca and Stephen Toulmin as well as Kenneth Burke’s concepts of identification and expectation. I illustrate that endorsement editorials reveal the underlying values and beliefs of a newspaper, both reflecting and constructing power relations within society. \nThis dissertation theorizes that endorsement editorials create persuasive arguments by combining deliberative discourse with forensic and epideictic rhetoric. Endorsement editorials debate the expediency of a course of action, in particular electing a party or leader to govern, exemplifying deliberative discourse. But they also employ forensic discourse to justify their decisions on the basis of the past actions of the parties or candidates. In addition, epideictic rhetoric is used to praise or blame political participants. Although logos might be anticipated to predominate in a text dealing with the future of the country, endorsement editorials incorporate all three appeals, with pathos and ethos often the strongest, to produce compelling arguments.
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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 | 0.025 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".