Can rural America support a knowledge economy?” Economic Review (Third Quarter
Bibliographic record
Abstract
Knowledge has become the new premium fuel for economicgrowth in the 21st century. Knowledge fuels new ideas andinnovations to boost productivity—and to create new products, new firms, new jobs, and new wealth. Some analysts estimate that knowledge-based activity accounts for half of the gross domestic product in Western industrialized countries. In the United States, knowledge-based industries paced gross domestic product (GDP) growth from 1991 to 2001, and their importance has accelerated since 1995. In rural America, as elsewhere, a variety of factors make knowledge-based growth possible: high-skilled labor, colleges and universities, vibrant business networks, and infrastructure. Some rural communities are already leveraging these assets to transform their economy. Many other rural places, however, have yet to tap this rich economic potential. This article analyzes the factors essential to rural knowledge-based activity in rural America. The first section defines knowledge-based eco-nomic activity, describes its growing importance in the U.S. economy, and identifies the regions of the country where it is concentrated. The Jason Henderson is an economist in the Center for the Study of Rural America at the Federal Reserve Bank of Kansas City. Bridget Abraham is a former research associate
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads 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".