The handbook of election news coverage around the world - Edited by Jesper Strömbäck & Linda Lee Kaid
Bibliographic record
Abstract
This handbook offers a sustained look at news coverage of elections around the world. The initial chapter lays out issues concerning media and governmental systems around the world, appropriately stressing the interdependence of these systems. Then the editors convincingly explain why news coverage of political campaigns is an important topic for scholarship. The chapters that follow provide considerable information about the governments, the media, and the news coverage of election campaigns in 22 countries and the European Union Parliamentary elections. The final chapter works to draw together the threads used to open the book; the editors offer insightful conclusions about the chapters covered in the book. The chapters of this book vary in the choice of specific topics covered. Many chapters provide a discussion of the historical development of governmental and media systems in a country—an important feature because few scholars are likely to be equally knowledgeable about the many countries covered in this handbook. Chapters offer different levels of attention to the framework presented in the introductory chapter. Some chapters offer previously unpublished data (e.g., the chapters on Brazil, Poland, or Sweden); some chapters report data from published sources (such as the chapters about Germany, Italy, or the Netherlands); others offer more of a narrative literature review (e.g., the chapters covering Canada, India, or Serbia). Many chapters offer tables, figures, or charts reporting data, although others lack these features. News frames (horse race, issue, and scandal) are discussed in over half of the chapters; agenda-setting, priming, polling, media effects, negativity of coverage, personalization of campaigns, and bias of coverage appear in fewer than half of the chapters. A table in the concluding chapter (26.2) which summarizes results from the earlier chapters notes that we need to interpret this table “with caution” because definitions and approaches” for content analysis of type of coverage (news frames) vary across chapters. A trivial example of these chapters' heterogeneity, but one which illustrates the point, concerns how the various tables report data over time. Some tables present data from earlier years in the left-most data column and recent years on the right-most data column; some of the tables reverse this order. Other tables are organized so years appear in rows rather than in columns. Of course, to some extent, a diversity of approaches and topics is inevitable given the variety of governmental and media systems along with the widely disparate amounts of literature available about election news coverage in these countries. However, greater consistency in focus of the chapters would have strengthened this collection and helped readers develop comparisons between chapters, making similarities and trends easier to identify.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".