Towards an Experientialist Understanding of Journalism: Exploring Arts-based Research for Journalism Studies
Bibliographic record
Abstract
In this paper, we explore the ways in which we can employ arts-based research methods to unpack and represent the diversity and complexity of journalistic experiences and (self) conceptualisations. We address the need to reconsider the ways in which we theorise and research the field of journalism. We thereby aim to complement the current methodologies, theories, and prisms through which we consider our object of study to depict more comprehensively the diversity of practices in the field. To gather stories about journalism creatively (and ultimately more inclusively and richly), we propose and present the use of arts-based research methods in journalism studies. By employing visual and narrative artistic forms as a research tool, we make room for the senses, emotion and imagination on the part of the respondents, researchers and audiences of the output. We draw on a specific collaboration with artists and journalists that resulted in a research event in which 32 journalists were invited to collaboratively recreate the “richness and complexity” of journalistic practices.
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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 | 0.039 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.009 | 0.083 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".