Crowdfunding independent and freelance journalism: Negotiating journalistic norms of autonomy and objectivity
Bibliographic record
Abstract
In recent years, news organizations have been struggling to find viable business models, with many media outlets either closing or facing budget cuts, resulting in fewer journalists working with fewer resources. One solution that has been gaining momentum is the practice of crowdfunding. This move has been heralded as one that puts audiences in a position of power, in that they have a say in what journalists pursue, but how does the role of the professional journalist change when giving up some control of the news process? This article argues that journalists who crowdfund strongly believe in the journalistic norm of autonomy, but at the same time feel a great deal of responsibility towards their funders, which is potentially a point of conflict. They also challenge the journalistic norm of objectivity, often using crowdfunding as an avenue to create journalism with a ‘point of view’, or advocacy journalism.
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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 | 0.166 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.020 | 0.097 |
| Scholarly communication | 0.035 | 0.022 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".