Waiting for Data Journalism
Bibliographic record
Abstract
Data journalism has emerged as a trend worthy of attention in newsrooms the world over. Previous research has highlighted how elite media, journalism education institutions, and other interest groups take part in the emergence and evolution of data journalism. But has it equally gained momentum in smaller, less-scrutinized media markets? This paper looks at the ascent of data journalism in the French-speaking part of Belgium. It argues that journalism, and hence data journalism, can be understood as a socio-discursive practice: it is not only the production of (data-driven) journalistic artefacts that shapes the notion of (data) journalism, but also the discursive efforts of all the actors involved, in and out of the newsrooms. A set of qualitative inquiries allowed us to examine the phenomenon by first establishing a cartography of who and what counts as data journalism. It uncovers an overall reliance on a handful of passionate individuals, only partly backed up institutionally, and a limited amount of consensual references that could foster a shared interpretive community. A closer examination of the definitions reveal a sharp polyphony that is particularly polarized around the duality of the term itself, divided between a focus on data and a focus on journalism, and torn between the co-existing notions of “ordinary” and “thorough” data journalism. We also describe what is perceived as obstacles, which mostly pertain to broader traits that shape contemporary newsmaking; and explain why, if data journalism clearly exists as a matter of concern, it has not transformed in concrete undertakings.
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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 | 0.042 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".