Between Objectivity and Openness—The Mediality of Data for Journalism
Bibliographic record
Abstract
A number of recent high profile news events have emphasised the importance of data as a journalistic resource. But with no definitive definition for what constitutes data in journalism, it is difficult to determine what the implications of collecting, analysing, and disseminating data are for journalism, particularly in terms of objectivity in journalism. Drawing selectively from theories of mediation and research in journalism studies we critically examine how data is incorporated into journalistic practice. In the first half of the paper, we argue that data's value for journalism is constructed through mediatic dimensions that unevenly evoke different socio-technical contexts including scientific research and computing. We develop three key dimensions related to data's mediality within journalism: the problem of scale, transparency work, and the provision of access to data as 'openness'. Having developed this first approach, we turn to a journalism studies perspective of journalism's longstanding "regime of objectivity", a regime that encompasses interacting news production practices, epistemological assumptions, and institutional arrangements, in order to consider how data is incorporated into journalism's own established procedures for producing objectivity. At first sight, working with data promises to challenge the regime, in part by taking a more conventionalist or interpretivist epistemological position with regard to the representation of truth. However, we argue that how journalists and other actors choose to work with data may in some ways deepen the regime's epistemological stance. We conclude by outlining a set of questions for future research into the relationship between data, objectivity and journalism.
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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 | 0.012 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".