Through a Lens Darkly: How the News Media Perceive and
Bibliographic record
Abstract
Studying news is a guarantee against running out of research ideas. Who could have predicted the uproar over the behaviour of a Canadian prime minister at a Catholic funeral for a former Governor General? Perhaps only David Haskell, author of a new book, Through a Lens Darkly: How the News Media Perceive and Portray Evangelicals, that examines how Canadian journalists report on evangelical Christians. Set against a backdrop that includes media criticism in 2000 of the creationist beliefs of former Canadian Alliance leader Stockwell Day, David Haskell draws not only on a major study of ten years of transcripts of national Canadian television news reporting (from Global, CTV and CBC), but also on his own experience as a journalist. John Schmalzbauer’s excellent 2003 book, People of Faith: Religious Conviction in American Journalism and Higher Education, treads similar territory. But there is little research on the attitudes of Canadian journalists with regard to religion, which makes Haskell’s work noteworthy. Haskell takes the necessary first step of defining and describing evangelicals, his
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.030 | 0.029 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".