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
In this paper I present a methodology to provide uncertainty measures at the regional level in real time using the full bandwidth of news. In order to do so I download vast amounts of newspaper articles, summarize these into topics using unsupervised machine learning, and then show that the resulting topics foreshadow fluctuations in economic indicators. Given large regional disparities in economic performance and trends within countries, it is particularly important to have regional measures for a policymaker to tailor policy responses. I use a vector-autoregression model for the case of Canada, a large and diverse country, to show that the generated topics are significantly related to movements in economic performance indicators, inflation, and the unemployment rate at the national and provincial level. Evidence is provided that a composite index of the generated diverse topics can serve as a measure of uncertainty. Moreover, I show that some topics are general enough to have homogenous associations across provinces, while others are specific to fluctuations in certain regions.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it