‘You can’t buy a revolution, but you can support a paper fighting for one’: Journalism cooperatives’ organizational traits and journalistic missions
Bibliographic record
Abstract
This article explores journalism cooperatives, a sub-field of journalism practice and a type of news organization that is becoming more popular as an organizational option in journalism internationally. While other non-traditional ways of funding and producing news - such as non-profit media or crowdfunding initiatives - receive growing attention from journalism researchers, cooperative enterprises remain largely neglected. This study develops an inventory of 29 such news outlets in Europe, North America, and South America. It presents a portrait of the cooperative field in journalism in terms of founding year, geographic location, linguistic tendencies, and cooperative sub-types. Second, based on an analysis of the official websites of seven of these cooperatives, the article describes the revenue sources, workforce, governance structures, and missions of such needs-rather than profit-driven news outlets. The study finds that their organizational traits oscillate between alternative media, non-profit media, and mainstream media attributes. However, in addition, journalism cooperatives possess a unique feature, namely internal democratic governance mechanisms, which have the potential to increase public trust in the news media. Moreover, for the seven cooperatives investigated here, this potential is enhanced further as they assign a key role to audiences as owners, managers, and funders of the organization (they are either reader-owned or co-owned by journalists and readers). I argue that the results of this exploratory study on journalism cooperatives, together with existing research on news non-profits, invite us to consider that the social economy might be a better default home for journalism than the capitalist marketplace.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".