Bibliographic record
Abstract
First paragraphs: As a scholar working with the Regional and Rural Broadband research team in Canada (see http://www.r2b2project.ca), I was motivated to review Responsive Countryside: The Digital Age and Rural Communities, by Roberto Gallardo, to learn more about digitally engaged rural community development in the U.S. I begin this review with Gallardo's contextual discussion of the U.S. countryside. I then consider Gallardo's examples of digital revolutions in rural community development and finally reflect on this book's scholarly contributions. In defining the term "rural" in Chapter 1, Gallardo clearly appreciates that, unlike in the past, businesses and livelihoods in the countryside are not only about agriculture. Rural is a geographic concept that connotes location and lifestyle. In the U.S., there have been profound changes in rural areas (those without an urban core of at least 10,000 residents) and small cities (those with an urban core of 10,000 to 49,999 residents). Gallardo produces an evidence base that, contrary to general perceptions, the population in the U.S. countryside is growing. This also applies to rural parts of other countries, such as Canada. However, population growth rates in the countryside are slower than in metro areas. The U.S. population is also aging, and rural communities and small cities are aging faster than metro areas. Further, the U.S. population is becoming more diverse, with a decrease in white non-Hispanics and an increase in Hispanics, even in rural areas. Gallardo argues that these changes are due to new technologies, not the least of which are digital revolutions.....
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".