Mapping Macroeconomics - An interactive platform for the exploration of inter-specialties relationships in macroeconomics 1970 - 2020
Bibliographic record
Abstract
The Mapping Macroeconomics project is an online interactive platform displaying bibliometric data on a large set of macroeconomic articles. It aims to offer a better understanding of the history of macroeconomics through the navigation between the different bibliometric networks. The point of departure of the project is the observation of an exponential increase in the number of articles published in academic journals in economics since the 1970s. This phenomenon makes it harder for historians of economics to properly assess the trends in the transformation of economics, the main topics researched, the most influential authors and ideas, etc. We consider that developing collective quantitative tools could help historians to confront this challenge. The opportunities that a quantitative history brings are particularly useful to the recent history of macroeconomics. Practicing macroeconomists are eager to tell narratives of the evolution of their field that serve the purpose of intervening on current debates, by giving credit to particular authors and weight to specific ideas. Historians who go into this area find plenty of accounts by macroeconomists and have to handle the vast increase in the macroeconomic literature since the last quarter of the past century. The Mapping Macroeconomics platform aims at helping historians to empirically check macroeconomists’ narratives on the discipline, to explore interesting patterns on the evolution of macroeconomics, and eventually to write new histories of macroeconomics.
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.
How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".