Trade Weighted Effective Exchange Rate for the
Bibliographic record
Abstract
dollar crashed through the US$1 barrier, and stood at par with the U.S. dollar for the first time since 1976. The euphoria of this achievement was then subsequently eclipsed on October 31, 2007 when the loonie was pushed beyond the previous post-1950 high of US$1.0614 (C$0.9422) – which was set in August 1957 3 – chalking up a new high mark at US$1.1030 (C$0.9066) on November 7, 2007. Indeed, while the ascent in the value of the loonie could be traced back to 2002 (when it was at its lowest value against the greenback), the rapid appreciation in 2007, and in particular, the stratospheric jumps in September and October that year, have been spectacular by all measures. No wonder that the loonie was named the “Canadian Newsmaker of 2007 ” by a leading U.S. news magazine. Since then, however, the CAD has weakened noticeably. In fact, by the end of 2007, the Canadian dollar had fallen back below parity with the U.S.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.024 |
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".