Bibliographic record
Abstract
Where are the jobs? That anxious question pervading national discussions of the Great Recession and its aftermath is becoming acute in the Intermountain West. Not only has the region’s usual faster-than-the-nation employment snapback after recessions failed to materialize this time around. What is more, the Mountain region’s halting economic recovery in some ways actually weakened in the first three months of 2010 as reports this new edition of the Mountain Monitor, a quarterly report produced by Brookings Mountain West, a partnership between Brookings and the University of Nevada, Las Vegas (UNLV), and a companion product to Brookings national MetroMonitor. Drawing on data covering the first quarter of 2010 (ending in March), the new picture is sobering and surveys a region that is at once recovering and still struggling. Output growth slowed in the region’s largest metropolitan areas. Employment fell faster in the Intermountain West than it did nationally and—frustratingly—faster than it did last quarter. Unemployment, for its part, inched upwards in every large metro except Denver. And for that matter, the overhang of real estate-owned properties in the Intermountain West—a measure of foreclosure actions—actually increased during the first quarter of 2010 in almost every large metro. In short, for the first time in three decades the region finds itself unable to lead the nation out of a recession and forced into a period of serious questioning about the sources of future growth with further federal stimulus unlikely. In these new, uncharted territories, certain corners of the Mountain West face the prospect of being left behind the rest of the country and virtually all of the region’s metropolitan areas have to reevaluate the basics of the Western growth model. Hence this report: To inform deliberations this edition of the Mountain Monitor examines data on employment, unemployment, output, home prices, and foreclosure rates for the Intermountain West’s 10 large metropolitan areas, the nation’s 100 largest metros, and 21 smaller metros dispersed around the Mountain region, as it was available through the first quarter of 2010.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.456 | 0.161 |
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".