All together now: Why the future of Canadian journalism education needs collaboration – and lots of it
Bibliographic record
Abstract
Many journalists were trained in a milieu where competition, often fierce, was the norm. But recently, in the face of urgent technological, economic and existential crises, newsrooms are collaborating with former competitors and other civic organizations in ways they may not have previously considered. Similarly, Canadian journalism educators are leading collaborative efforts on large and small scales. There is no clear road map yet for these partnerships, but there is a growing body of research and practice that suggest collaboration can help with the quality of investigative journalism and connect with communities in new and liberating ways. For educators who wish to incorporate real-world collaboration in their classrooms, there are resources available to help with both the theory and skills needed to work well with others.
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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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.049 | 0.028 |
| Scholarly communication | 0.049 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".