The Awfulization of News: When the Real World of Technology Meets the Real World of Journalism
Bibliographic record
Abstract
During a 2014 meeting with the journalism fellows at Massey College, Ursula Franklin presciently warned against what she called the 'awfulization' of public discourse—a ceaseless tallying of wrongs, with no path to setting it right. Understanding, not simply awareness, is the key to creating change, she said. Reflecting on Franklin's published work and personal conversations with her, as well as more than two decades of experience reporting on Indigenous issues in Canada, this critical essay examines how mediatization and Franklin's concept of the bit-sphere influences our understanding of uncertainty and our capacity for a peaceful future. It asks whether digital news media is training citizens to be indifferent to the suffering of others, leading Canadian society further away from the peace Franklin sought. In particular, it considers the possibility that Canadian journalists working in the bit-sphere are habituating the public to substitute awareness for action on racial injustices against Indigenous peoples. Franklin's 1989 Massey Lectures form the framework for this creative non-fiction work, which uses personal examples from the real world of reporting in Canada and media criticism from Indigenous scholars including Candis Callison, Daniel Heath Justice, Lee Maracle and others, to argue that the bit-sphere is rendering news media incapable of facilitating public understanding for collective action. In The Real World of Technology, Franklin foreshadowed three dangers posed to society by changing communications technology: the risk of pseudo realities and a lack of reciprocity within them; the potential for the bit-sphere to flatten all communication into information delivery; and the loss of the biosphere's valuable 'by-the-way learning'.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.078 |
| Scholarly communication | 0.046 | 0.033 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".