Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it