The Visual “Big Picture” of Intermediate Macroeconomics: A Pedagogical Tool to Teach Intermediate Macroeconomics
Bibliographic record
Abstract
The primary purpose of this paper is to introduce a holistic, visual “big picture” of the concepts and diagrams that are commonly covered in the course of intermediate macroeconomics. Intermediate macroeconomics discusses numerous concepts and diagrams in order to finally show how aggregate supply (AS) and aggregate demand (AD) are derived in an economy. A further learning objective defined for the intermediate macroeconomics course is to enable students to investigate the overall effects of macroeconomic policies on AS and AD in the economy. In order to better attain the aforementioned learning objectives, the present paper proposes a visual “big picture” which can be applied as a pedagogical tool in teaching intermediate macroeconomics classes. This visual “big picture” logically connects twenty-seven macroeconomic diagrams which are usually introduced in the intermediate macroeconomics course, and also describes the general pattern and overall structure of macroeconomics in terms of four separate markets in a visual way, namely labor market, capital market, money market, and goods market. Finally, it is suggested that this visual “big picture” should be provided to the students taking the course of intermediate macroeconomics so that they can readily grasp the logical order of the concepts and the underlying complex structure of the markets that are discussed in the course.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".