Using Source Tracking AI to Analyze News Coverage about First Nations, Indigenous and Métis Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p> “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 </p>
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it