A Force for Good: How the American News Media Have Propelled Positive Change
Bibliographic record
Abstract
Streitmatter, Rodger. A Force for Good: How the American News Media Have Propelled Positive Change. Lanham, Md.: Rowman & Littlefield, 2015. 229 pp. $36.For most journalists, it's self-evident that their work makes a difference in our democracy. Rodger Streitmatter has selected examples of news coverage to show the ways in which journalism made a difference in the United States of America for more than one hundred years. He calls it propelling positive change. The sixteen examples demonstrating his argument include: coverage of Ellen DeGeneres coming out as a lesbian, Japanese-Americans seeking reparations for their internment during World War II, Jackie Robinson breaking the color barrier in major league baseball, and Bess Myerson becoming the first (and so far only) Jewish Miss America. Each chapter is an instance of journalism aimed at improving the lives of a segment of society.Streitmatter is careful to bracket his examples of outstanding journalism that helped along positive social change. In fact, he notes that some of the work could be seen as unethical because the journalists crossed the boundaries of standard practice. For example, some of the journalists covering Jackie Robinson, and their editors, intentionally diminished or ignored the ways in which Robinson was mistreated because he was black.The glowing coverage of Robinson began when he was signed by the Montreal Royals. The Baltimore Sun put its story about his acquisition by the Brooklyn Dodgers farm team on the front page in 1946 and so did the Chicago Tribune. When Robinson played his first, unremarkable game as a Brooklyn Dodger the next year The New York Times ran an editorial claiming he would have been playing sooner if he had been white, and praising the team's general manager for his courage. The news coverage during the season portrayed Robinson as humble, amiable, and cooperative. In the journalists' accounts, he was wholesome and had an exemplary private life. Any discouraging words were buried far down in the stories or not published at all. In fact, one team refused to take the field if Robinson was playing. Players from opposing teams stepped on Robinson, struck him with balls, and shouted racial epithets.Streitmatter credits the news coverage of DeGeneres with redefining the American lesbian. As in many chapters, a brief biography is included. He traces her public life from appearances on Johnny Carson's Tonight Show, to the television situation comedy in which the lead character came out as a lesbian, through her afternoon talk show, her marriage, and the extensive, positive news coverage along the way. …
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.020 | 0.018 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".