Conceptualizing Mediatization: Contexts, Traditions, Arguments
Bibliographic record
Abstract
Why “mediatization” as a topic for communication theory now? This rather ungainly word has been rising in prominence for the past decade, but many readers of this journal may still want to ask: What does it mean? What does it add to communications theory? And is it necessary at all? The purpose of this introduction to the special issue—apart from introducing and summarizing the articles that follow—is to provide some context for the emergence of “mediatization” as a key theoretical concept for contemporary media and communications research, and to offer some reasons why it now deserves the full attention of scholars of communication theory. The word “mediatization” does indeed strike an odd note to the ears of native English speakers, provoking the immediate reaction: Why is such an awkward formulation needed? But we are long past the point in communications research when the instincts of native English speakers should have priority! A fully institutionalized field of communications research has a pressing need for common terms that can orientate researchers from many countries and geolinguistic regions toward shared problems and areas of inquiry. As Sonia Livingstone (2009) explained in her ICA Presidential address in Montreal in 2008, “mediatization”—or in German: Mediatisierung—is a term with a long and respectable history in German-speaking countries. More important, it has emerged as the most likely “winner” in a race between many terms, all cumbersome or ambiguous to varying degrees—mediazation, medialization, mediation—that have been coined to capture somehow the broad consequences for everyday life and practical organization (social, political, cultural, economic) of media, and more particularly of the pervasive spread of media contents and platforms through all types of context and practice. Put simply, something is going on with media in our lives, and it is deep enough not to be reached simply by accumulating more and more specific studies that analyze this newspaper, describe how that program was produced, or trace how particular audiences make sense of that film on a particular occasion.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.147 |
| Scholarly communication | 0.024 | 0.052 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| 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".