Reimagining Canadian Journalism: A Case Study of News Startups in Montreal
Bibliographic record
Abstract
The Canadian journalism industry has been in a self-described crisis, partly of its own making, for arguably as long as it has existed. With each new technological advancement comes a new wave of self-doubt, and in the past five years, many legacy news outlets have turned to the federal government for financial help. At the same time, a new crop of digital news startups has grown. Their numbers fluctuate, but at least 270 digital-first outlets existed in Canada as of last year (LION Publishers, 2023). These outlets tend to be more independent and less nostalgic about journalistic norms and traditions, forging new approaches to information-gathering and sharing. As trust in news media has generally eroded (Reuters, 2024), many news startups have responded by focusing on hyperlocal or niche information needs, and on direct audience engagement. Some are also exploring philanthropic funding opportunities or seeking out non-profit status, effectively turning the capitalist, ad-revenue-driven newspaper model on its head. Through a series of three 30- to 50-minute podcasts, three Montreal-based case studies – La Converse, The Rover and Pivot – are examined to help answer the question: As Canadian legacy news media crumble, how will the new kids on the block respond? Judging by interviews with the founders of the three news outlets, the next generation of journalism leaders value slowing down to conduct more long-form and investigative reporting; spotlighting voices that have been traditionally marginalized in the media; and, for some, favouring radical transparency over neutral objectivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.047 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".