Updating USAGE: Baseline and Illustrative Application
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 it