Discourse lines:visualising current policy and media storylines of opportunity and disadvantage with narrative exploration maps
Bibliographic record
Abstract
Topics of disadvantage are often discussed in the media. The discourse<br/>of disadvantage is multidimensional and has many intersecting<br/>elements, with some issues more common than others (e.g. violence,<br/>addiction), and some tending to co-occur, like human rights,<br/>criminal justice, and health to name just a few common themes.<br/>Here, we introduce and describe “Discourse Lines”, an online interactive<br/>visualisation to discover which co-occurring disadvantage<br/>issues are being discussed in the media, and which ones are left out and obscured. The visualisation presents an AI-assisted analysis of<br/>news articles on topics of discourse. Our multi-scale architecture<br/>metro map visualisation invites users to drill down from a topics<br/>overview landing map to topic-specific metro maps until individual<br/>news articles. This dynamic platform allows users to see how discourse<br/>on topics of disadvantage unfolds and how news conflates or<br/>separates various issues over time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 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".