Updating USAGE: Baseline and Illustrative Application
Bibliographic record
Abstract
USAGE is a dynamic, CGE model of the U.S. economy created at CoPS in collaboration with the U.S. International Trade Commission (USITC). The model has been used by and on behalf of: the USITC; the U.S. Departments of Commerce, Agriculture, Energy, Transportation and Homeland Security; private sector organizations such as the Cato Institute and the Mitre Corporation; and the Canadian Embassy in Washington DC. To keep the model relevant for policy analysis, it must be updated periodically. This paper describes a major update of USAGE undertaken for the USITC. In accordance with the CoPS contract with the USITC, the update task was to: 1. build a NAICS-based database at the 400-industry level for USAGE using the 2007 BEA benchmark input-output tables; 2. update this database to 2014; 3. create a baseline projection starting from the base year of 2014 and proceeding at 5 year intervals to 2024; and 4. conduct an illustrative USAGE policy simulation around the baseline. At the completion of this work in August 2016, the ITC requested a fifth task: 5. update from 2007 to 2015 rather than 2014 and create a baseline from 2015 to 2020. This paper describes how we undertook the five tasks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".