Bibliographic record
Abstract
This study attempts to forecast Canadian unemployment rates using aggregated internet search data from Google Trends. The three forecasted versions of the unemployment rate, obtained from Statistics Canada’s Labour Force Survey, are the official rate, the youth (15-24 years old) rate, and the supplementary rate which includes discouraged and involuntary part-time workers. Google Trends forecasting variables include the aggregated search category ‘Job Listings’ as well as the individual search terms ‘Employment Insurance’ and ‘Employment’. Taking the weekly search indices and averaging them over various weeks to associate them with the monthly unemployment rates, numerous unemployment rate-search index models were produced. Using Johansen’s test, models whose variables are determined to be cointegrated are then estimated using the error correction model. Models with long- run equilibriums with the lowest RMSEs are then used to produce out-of-sample forecasts of the unemployment rates. The results are that the ‘Job Listings’ search category gave the lowest RMSE and produced accurate out-of-sample forecasts for both the official and supplementary unemployment rates; however, no model could accurately predict the large increase in unemployment rates caused by the economic downturn of late 2008 to early 2009.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".