Bibliographic record
Abstract
The type of empirical research that was a part of the institutionalist program often required significant financial support for data gathering, research assistants, and other costs – costs not involved in “armchair” theorizing. In the period before World War I, there were few sources of such funding because universities themselves did not usually provide significant funding for social science research. As mentioned in Chapter 7, the Pittsburgh Survey was funded by the Russell Sage Foundation, and Commons's early work on the history of trade unionism was funded in part by the Carnegie Institution, but this was quite unusual. In the period immediately following the end of World War I, there was a massive upsurge of optimism concerning the possibilities and social benefits that could flow from a properly “scientific” approach to the social sciences. A great deal of this optimism came from the experience of wartime planning, including the development of data sources, data analysis, policy appraisal, and the exercise of a degree of economic control. Some of the more overt expressions of this were the founding of The National Bureau of Economic Research (1920), The Institute of Economics (1922), and the Social Science Research Council (1923). All of these organizations were dominated by institutional economists or other social scientists of similar viewpoint. These developments were made possible only by a matching willingness of a number of foundations to fund social science research.
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 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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.074 | 0.027 |
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".