Bibliographic record
Abstract
The concept of transmediation might be one of the most influential intellectual tools for studying, discussing and fostering innovative phenomena across fields of study. One could argue that the term itself is self-explanatory. Etymologically, it suggests a transition (to go across) between different media but the concept of transmediation has been shaped both by the somewhat historically lax definition of media and the disciplines that have adopted the term. And while the looseness of media gives transmediation a potential for interdisciplinarity, the siloed nature of academic disciplines could have, at the same time, hindered such potential. In this paper, we introduce a framework for transmediation based on an analysis of the use and evolution of the concept and our own explorations over the last few years. This framework aims to provide a common language and a set of conceptual prompts to explore the notion of transmediation further.
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.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".