Putting theory to practice A critical approach to journalism studies
Bibliographic record
Abstract
There has been considerable debate over the proper place of journalism education within the academy. We argue that programmes which compromise between vocational training and a broader programme of study based in the liberal arts remain unsatisfactory because they put too much onus on students themselves to bridge the gap between theory and practice. Taking up James Carey's challenge to more precisely locate the object of study, we believe journalism education must begin from a view of journalism as an institutional practice of representation with its own historical, political, economic and cultural conditions of existence. This means that the journalism curriculum must not only equip students with a particular skill set and broad social knowledge, but must also show students how journalism participates in the production and circulation of meaning.
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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 | 0.076 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.014 | 0.125 |
| Scholarly communication | 0.042 | 0.036 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".