From Peripheral to Integral? A Digital-Born Journalism Not for Profit in a Time of Crises
Bibliographic record
Abstract
This article explores the role of peripheral actors in the production and circulation of journalism through the case study of a North American not-for-profit digital-born journalism organization, The Conversation Canada. Much of the research on peripheral actors has examined individual actors, focusing on questions of identity such as who is a journalist as opposed to emergent and complex institutions with multiple interventions in a time of field transition. Our study explores the role of what we term a ‘complex peripheral actor,’ a journalism actor that may operate across individual, organizational, and network levels, and is active across multiple domains of the journalistic process, including production, publication, and dissemination. This lens is relevant to the North American journalism landscape as digitalization has seen increasing interest in and growth of complex and contested peripheral actors, such as Google, Facebook, and Apple News. Results of this case study point to increasing recognition of The Conversation Canada as a legitimate journalism actor indicated by growing demand for its content from legacy journalism organizations experiencing increasing market pressures in Canada, in addition to demand from a growing number of peripheral journalism actors. We argue that complex peripheral actors are benefitting from changes occurring across the media landscape from economic decline to demand for free journalism content, as well as the proliferation of multiple journalisms.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.028 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".