Media Tactics in the Long Twentieth Century
Bibliographic record
Abstract
Integrating media studies with history, Media Tactics in the Long Twentieth Century explores the dynamic relationship between tactics and strategies in recent history. Drawing on examples from a range of different countries and world regions, and looking at the infrastructures, entanglements, and institutions involved, the volume makes a strong case for media tactics as a new field of scholarly inquiry and for the importance of a historically informed approach. In contrast to strategic communication approaches, this media historical intervention contributes to new knowledge about the practical implementation of strategies. First foregrounding tactics as an object of study, the volume then counters the presentism of contemporary studies by adding a necessary historical perspective. Moreover, the book theoretically disentangles the concept of strategy – from an abstract contemporary buzzword to concrete, hands-on actions – which in turn reveals the complexity of using media strategies and media tactics in reality. This volume will interest scholars and students working in the field of media and communication in general, and in the subfields of strategic communication, public relations, media history, and propaganda studies. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND)] 4.0 license.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".