Bibliographic record
Abstract
This course introduces the process of undertaking empirical research in economics leading to the formulation and preparation of a research proposal. General Goals This course introduces you to economic methodology, research design, modes of observation, and data presentation. Each of these concepts is described, and then you apply them in a specific empirical research proposal. You will study how economists attempt to verify the way that they understand economic problems. For example, they might predict that a rise in the money supply will increase the inflation rate, all else the same, or that the foreign-born has higher incomes than the Canadian-born, all else the same. How do economists go about verifying these predictions? The course describes some of the methods. I suppose that the overall goal of this course is to affect your way of thinking about empirical2 research in economics. My general belief is that while you and I sometimes produce empirical research, we are more often consumers of that research. As producers, we sometimes undertake empirical research that deals with some economic problem (such as in the research proposal for this
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.823 | 0.724 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".