Bibliographic record
Abstract
The Mountain West’s recovery from the Great Recession is spreading. Output is growing in every metropolitan area. Still, hiring remains elusive—a fact frustrating the entire nation, but perhaps more so in a region used to snapping, even roaring, back from recessions faster than the rest of the nation. Drawing on data covering the fourth quarter of 2009 (ending in December), this new Mountain Monitor—a companion product to Brookings’ national MetroMonitor and a quarterly resource produced by Brookings Mountain West, a partnership between Brookings and the University of Nevada at Las Vegas—surveys a region that is at once recovering and still struggling. Previously, the Intermountain West has been almost impervious to national declines over the last 30 years. Construction growth looked automatic. Job growth seemed a given. And when there were recessions as in the 1980s and 1990s they were—in retrospect—mere blips in a steady upward trajectory. And yet now, the region’s very strengths have become its greatest weaknesses as what was taken for granted now remains elusive. For example, the once unstoppable housing sector now represents one of the heaviest drags on the region’s recovery, dampening the usual rapid snap-back of hiring. In sum, the region is having to consider what may fuel the next era of growth, job creation, and broadly shared prosperity. To this end, this edition of the Mountain Monitor examines data on employment, unemployment, output, home prices, and foreclosure rates for Intermountain West’s 10 large metropolitan areas, the nation’s 100 largest metros, and 17 smaller metros dispersed around the Mountain region through the fourth quarter of 2009.
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 | 0.000 | 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) | 0.401 | 0.187 |
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".