Dealing with the mess (we made): Unraveling hybridity, normativity, and complexity in journalism studies
Bibliographic record
Abstract
In this article, we discuss the rise and use of the concept of hybridity in journalism studies. Hybridity afforded a meaningful intervention in a discipline that had the tendency to focus on a stabilized and homogeneous understanding of the field. Nonetheless, we now need to reconsider its deployment, as it only partially allows us to address and understand the developments in journalism. We argue that if scholarship is to move forward in a productive manner, we need, rather than denote everything that is complex as hybrid, to develop new approaches to our object of study. Ultimately, this is an open invitation to the field to adopt experientialist, practice-based approaches that help us overcome the ultimately limited binary dualities that have long governed our theoretical and empirical work in the field.
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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 | 0.076 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.151 |
| Scholarly communication | 0.026 | 0.044 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".