The Journalistic Field and the City: Some Practical and Organizational Tales about the <i>Toronto Star</i> 's New Deal for Cities
Bibliographic record
Abstract
This article presents Pierre Bourdieu's field theory as a way to approach the under–theorized relationship of journalism and the city. The concept of field provides a way to conceive of the conditions of possibility for what journalists do in, through, and in relation to the urban. Bringing this concept together with practice theory and organizational sociology, I examine four practical and organizational tales–two narratives and two episodes–related to the Toronto Star's New Deal for Cities campaign. These tales demonstrate how journalistic practices are not only performed in and distinctively oriented towards urban space, but also are at the same time regulated by, oriented towards, and positioned in the journalistic field. I highlight how journalistic practices take place in multiple organizational sites, through changing regimes of managerial authority and legitimacy, and with shifting positioning in and orientations to the journalistic field and other social fields of the city.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.029 | 0.055 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".