A framework for examining hybridity: The case of academic explanatory journalism
Bibliographic record
Abstract
Across a number of disciplines, hybridity is regularly invoked when two previously distinct elements - whether objects, concepts, frameworks, practices, models, mediums or institutions - are brought together. However, this is often done with a vague theoretical nod. Labeled a hybrid and left at the level of broad theory, scholarship has tended to ignore a critical issue: what happens when the disparate elements of a hybrid are introduced in practice? This conceptual paper takes the case of academic explanatory journalism, a nascent intentional collaborative practice between academic authors and journalist editors, in order to illustrate how the theoretical concept of hybridity plays out in practice. This particular case presents a number of opportunities and benefits within a Western democratic context. However, our examination highlights that without a more nuanced discussion of how hybridization plays out in real life, its potential benefits are compromised. We propose a five-step framework that can be applied to other examples of hybridity, across varied disciplines beyond media and communication studies. This five-step framework helps uncover the complications that might arise when disparate elements are hybridized, moving from theory into practice. The approach helps create the space and understanding needed to design solutions pre-emptively.
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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 | 0.027 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.107 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".