Bibliographic record
Abstract
Abstract The book is about how journalists know what they know, who gets to decide what good journalism is, and how we know when it’s done right. Until a couple decades ago, these questions were rarely asked by journalists. When journalists were questioned by malcontented publics and critics about how they were doing journalism, these questions were easily ignored. Now, if you’re on social media, you’re likely to see multiple critiques of journalism on a daily basis. It seems not only convenient but pragmatic to give most of the credit to digital technologies and/or market failure for how relationships between journalists and diverse audiences have changed. This book rests on a different assumption, however. We contend that technologies offer a diagnostic to understand much deeper, persistent, and structural problems confronting journalism. Counter to much of the recent journalism scholarship, we argue that you can’t talk about the role journalists and journalism organizations could, should, and have played in society without talking about gender, race, other intersectional concerns—and settler-colonialism. Drawing on mixed methods and ethnography as well as interdisciplinary scholarship, this book examines the reckoning under way between journalists, their methods and their audiences in sites as diverse as social media, legacy newsrooms, journalism startups, novel forms of journalism memoir, and among indigenous journalists. The book explores journalism’s long-standing harms alongside repair, reform, and transformation. It suggests that a turn to strong objectivity and systems journalism provides a path forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.049 | 0.021 |
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".