Mediatization and the Absence of the Environment
Bibliographic record
Abstract
Abstract Mediatization remains a key concept for theorizing how our ever-evolving and intensifying media and communications environment underwrites and (re)constructs our social world, yet the socio-ecological effects of mediatization processes remain relatively unacknowledged within this research field. However, mediatization must be conceptualized as a cogent process whose impact extends beyond the confines of the “media environment” to the natural environment. We make this argument by reviewing how three dominant traditions of mediatization scholarship: (a) institutionalist, (b) cultural/social constructivist, and (c) materialist conceptualize “the environment.” We argue that scholars rarely acknowledge the materialist dimension of mediatization despite it being a fundamental aspect of mediatization processes. Consequently, we bring discourse surrounding the materiality of mediatization to the fore by drawing on theories of materiality from media and communication studies in general and highlighting three material dimensions of mediatization processes in particular: (a) resources, (b) energy, and (c) waste. In doing so, we make explicit the implicit material dimensions of mediatization processes that have been largely overlooked but are directly linked to how we understand, theorize and react to the societal, cultural, economic, environmental transformations brought about by media.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".