Book Review: The Stewart/Colbert Effect: Essays on the Real Impacts of Fake News
Bibliographic record
Abstract
The Stewart/Colbert Effect: Essays on the Real Impacts of Fake News. Amarnath Amarasingam, ed. Jefferson, NC: McFarland, 2011. 198 pp. $40 pbk.Reviewed by: Samuel P. Wallace, University of Dayton, Dayton, OH, USADOI: 10.1177/1077699012472036One of my few firmly held opinions is that nothing spoils a perfectly good bratwurst like learning how it is produced. However, despite seeing episodes of Dirty Jobs and How It's Made detailing exactly that manufacturing process in all its glory, I have managed to cling to my enjoyment of sausage (although I admit to an occasional unpleasant flashback). I have similar feelings about humor. Among the useful things I learned in a rhetorical criticism class in graduate school was that great humor can be absolutely ruined by discussion and analysis. As such, it was with more than mild trepidation that I approached The Stewart/Colbert Effect: Essays on the Real Impacts of Fake News.This volume is a thoughtful collection of contributed essays, edited by Amarnath Amarasingam, a doctoral candidate at Wilfred Laurier University in Ontario, Canada, that attempts to a new theoretical perspective and to study the overall social significance of Jon Stewart and Stephen Colbert. The collection is divided into two parts: New Research and Theoretical Considerations. Part 1, the research portion of the book, details five academic studies focused on the following questions: Do The Daily Show and Colbert Report bring attention to science and technology issues in the same way and with the same impact as they bring to politics? To what extent does The Daily Show provide its audience with simple rules of thumb (heuristics) allowing it to form attitudes about issues without going to all the trouble to carefully consider the related facts? Has the impact of The Daily Show on young adults and their political engagement been overdramatized in popular culture? Are The Daily Show and Colbert Report actually the authentic (real) news and much of the mainstream political coverage inauthentic (fake)? Finally, does The Daily Show benefit democracy by holding politicians and journalists accountable, or does it undermine democracy by promoting disengagement and alienation from the political process? These are all great questions!There are noteworthy strengths and weaknesses in the studies in Part 1. A significant strength of all the essays is the quality of the literature reviews and theoretical underpinnings of the studies being reported. Many provide first-rate models for making an argument supporting a research question or hypothesis. In addition, perhaps especially because I am not familiar with research in this area, the reviews provided marvelous contextual foundations for understanding the nature and importance of each study. Likewise, the methods used in the studies were very clearly described and should be clear to even novice students of research. The weaknesses sometimes can be found in the choice and/or execution of the research methods, and in some cases conclusions were speculative and tangentially connected to the reported results.Nevertheless, both the strengths and the weaknesses make this book a valuable tool for classes in research methods or media analysis. It's challenging to teach the process of crafting a well-documented and well-argued literature review that provides a strong rationale for a study, but these readings generally provide good models for illustration of those qualities. As for the weaknesses, I do not mean to imply that this work is subpar: it is not. …
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.056 | 0.040 |
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".