How is your Toronto neighbourhood portrayed in the news? Check it out using these interactive maps
Bibliographic record
Abstract
Concerns about how neighbourhoods are portrayed in the news have surfaced regularly in the Toronto area over the years. But are those concerns valid? Interactive maps produced by the The Local News Research Project (LNRP) at Ryerson University’s School of Journalism are designed to help Toronto residents answer this question. The maps give the public access to data the research project collected on local news coverage by the Toronto Star and the online news website OpenFile.ca. The maps are based on the Toronto Star’s local news coverage published on 21 days between January and August, 2011. Researchers have found that a two-week sample of news is generally representative of news coverage over the course of a year (Riffe, Aust & Lacy, 1993). The data for OpenFile.ca, which suspended publishing in 2012, were collected for every day in 2011 between January and August. Click here to see the maps or continue reading to find out more about news coverage and neighbourhood stereotyping, how the maps work, and the role of open data sources in this project.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".