News Coverage of Mediated Racism and Democracy in Canada
Bibliographic record
Abstract
This chapter explores the news coverage of mediated racism and democracy in Canadian news media. Using topic modeling to analyze articles, we identified four major topics: (1) The Squad, (2) The two Michaels, (3) Maxime Bernier, and (4) Robert Mugabe as well as a few minor topics such as Thabo Mbeki, Barack Obama, George Floyd, Joerg Haider, and Kellie Leitch. We found that the majority of the topics discussing racism and democracy in Canadian news media were not centered around Canada but rather issues happening in America and overseas. This outward focus indicated the Canadian news media, specifically journalists, do not perceive racism as much of a threat in Canada. Our findings correspond with the two recurring themes apparent throughout our topics. First, racism is broadly discussed in the news media as a direct threat to democracy, apparent in attempted racially charged policies from political players. Second, there is an ongoing tendency to distance Canada&s;s democratic values and supposed lack of racism away from that of other countries. This chapter interrogates Khon Kaen&s;s evolution as a model for small and medium-sized city development, assessing past and current infrastructure projects and their impact. Thailand&s;s fourth largest city, Khon Kaen, plays a vital role as an economic, educational, and health hub in the traditionally underserved Isan region. Development began with the 1957 U.S.-funded ‘Friendship Highway’ and expanded through Thai government policies encouraging growth outside Bangkok, including establishing Khon Kaen University in 1964. Today, Khon Kaen&s;s ‘Smart City’ ambitions framed around light rail plans, and the city&s;s strategic location at the intersection of the Belt and Road Initiative and the Greater Mekong Subregion economic corridors underscore its goal of becoming a resilient, mid-sized urban center. The city&s;s balancing of centralized and localized efforts offers valuable insights for building the resilience of similar cities across the Global South, particularly within the framework of regional economic corridors.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.025 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".