Using Source Tracking AI to Analyze News Coverage about First Nations, Indigenous and Métis Communities
Bibliographic record
Abstract
“This paper explores the interdisciplinary, creative development of an artificial intelligence (AI) tool designed to analyze sourcing practices in journalism, with a focus on news coverage of Indigenous, First Nations, and Métis communities in Canada. Rooted in theories of journalistic routines, framing, and media representation, the tool categorizes sources into seven key types: political, authority, expert, organization, unaffiliated, media, and celebrity. Analysis of a corpus of articles of interest to Indigenous communities reveals statistically significant imbalances in sourcing practices. Political and institutional sources were overrepresented, while unaffiliated sources, representing grassroots or lived experiences, were underrepresented. These findings reflect persistent biases in Canadian media’s portrayal of Indigenous communities, reinforcing institutional narratives over diverse perspectives. While the AI tool offers a systematic method to identify and quantify such patterns, limitations in its current iteration temper its broader applicability. Despite these limitations, the tool demonstrates potential for promoting accountability in journalism by enabling newsrooms to critically assess and refine their sourcing practices. Future iterations should address these shortcomings by incorporating more inclusive training data, refining category definitions, and improving accuracy for underrepresented and misclassified groups. This work underscores the need for ethical and methodological rigour in developing AI tools to address systemic inequities in media coverage
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".