Journalism education in Europe and North America : an international comparison
Bibliographic record
Abstract
Preface, Romy Frolich and Christina Holtz-Bacha. Introduction, Lee Becker. ACADEMIC TRADITION: JOURNALISM EDUCATION AT UNIVERSITIES. Finland's History in a Nutshell, Matti Salokangas. The Spanish Case: A Recent Academic Tradition, Aires Vaz and Carios Barrere. Journalism Education in the United States, David H. Weaver. Journalism Education in Canada, Peter Johansen and Christopher Dornan. NON-ACADEMIC TRADITION: JOURNALISM EDUCATION AT JOURNALISM SCHOOLS. Between Literary Roots and Partisanship: Journalism Education in Italy, Paolo Mancini. The Development of Journalism in the Netherlands: A Century Long Duel Pitting Beleaguered Rejectionists Against Pro Education Components, Gabriella Superimposed Metaphors in the News. Why We Should Distrust Computers: METAPHOR IN CONTEMPORARY CULTURE. Is Life a Game? Notes on a Master Metaphor. Hardball and Softball as Metaphors. From the Road to the Fast Track: American Metaphors of Life. The Fast Food Franchise as a Metaphor McMetaphors. Confessions of a Metaphoraholic. The Generation X and Boomers Metaphors. The '90s-An Empty Metaphor Walting to be Filled. The Metaphors of the Market. METAPHORS IN EDUCATION AND KNOWLEDGE. Economic Metaphors in Education. STRUCTURE-The Intellectuals Metaphor. The Projection Metaphor in Psychology. The Jigsaw Puzzle as a Metaphor for Knowledge. LANGUAGE DESCRIBING ITSELF Our Inflationary Language. Stalking the Wild Metaphor. Is Language a Game? Metaphors by the Seashore. Author Index. Subject Index.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".