Henry Jenkins, Sam Ford, and Joshua Green, Spreadable Media: Creating Value and Meaning in a Networked Culture
Bibliographic record
Abstract
“If it doesn’t spread, it’s dead.” This catchy marketing slogan is, happily, only found on the book jacket of Spreadable Media. Henry Jenkins, Sam Ford, and Joshua Green take pains to avoid simplistic pronouncements and instead offer an encompassing and engaged discussion of the complex and diverse ways in which various forms of media are circulated in the so-called Web 2.0 era. Because of the lead author’s previous cutting-edge work on media fandom and participatoryconvergence culture, the co-authors’ professional involvement in strategic communications, and the book’s origins in the MIT-based Convergence Culture Consortium, the scope of the book is broad. Each of the eight chapters (including the introduction) is between 30 and 40 pages long and draws on a range of theoretical perspectives, empirical research, and examples. American entertainment media culture is at its core, with two parallel tracks on digital marketing and alternative media and political/social protest. Spreadable Media can also be considered an edited collection of sorts, with multiple references to the online “enhanced version,” which includes blog-style pieces by a number of scholars whose work is discussed. This approach and format are Spreadable Media’s greatest strengths but also prove to be its weaknesses in places.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".