A Critique of “Macroeconomic Impacts of Canadian Immigration … Using the Focus Model ” (Dungan, Fang and Gunderson, 2010) General Comments
Bibliographic record
Abstract
the FOCUS macroeconomic model of the University of Toronto to estimate the impact of a 100,000 per year increase in immigration over a ten-year period starting in 2012. The methodology relies on microeconomic information, much of which is dated, from earlier studies of the impact of immigration in Canada and other countries to gauge the microeconomic impact that is used to shock the various exogenous variables and equations of the model. As is often the case in such studies, the model overrides are the most important determinants of the simulation results as most, if not all, the important impacts of immigration are not built into the structure of the model. And this key fact will, of course, be ignored by those who will use the results to make the case for higher immigration. In this case in particular, it is assumed that all the new immigrants will find employment to the same degree as other Canadians, but at a wage reflecting the substantial discount reported in the 2006 Census. This is based in part on the assumption that the Canadian economy will be operating at a high level of employment over the 2012 to 2022 period of the simulations which can be characterized as full employment. This, of course, is an
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".