Bibliographic record
Abstract
CONTENTS Preface Overview PART A: STATE DATA Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming PART B: METROPOLITAN STATISTICAL AREA DATA Notes MSA Rankings and Employment and Unemployment rates New York-Newark-Jersey City, NY-NJ-PA Los Angeles-Long Beach-Anaheim, CA Chicago-Naperville-Elgin, IL-IN-WI Dallas-Fort Worth-Arlington, TX Houston-The Woodlands-Sugar Land, TX Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Washington-Arlington-Alexandria, DC-VA-MD-WV Miami-Fort Lauderdale-West Palm Beach, FL Atlanta-Sandy Springs-Roswell, GA Boston-Cambridge-Newton, MA-NH San Francisco-Oakland-Hayward, CA Phoenix-Mesa-Scottsdale, AZ Riverside-San Bernardino-Ontario, CA Detroit-Warren-Dearborn, MI Seattle-Tacoma-Bellevue, WA Minneapolis-St. Paul-Bloomington, MN-WI San Diego-Carlsbad, CA Tampa-St. Petersburg-Clearwater, FL St. Louis, MO-IL Baltimore-Columbia-Towson, MD Denver-Aurora-Lakewood, CO Charlotte-Concord-Gastonia, NC-SC Pittsburgh, PA Portland-Vancouver-Hillsboro, OR-WA San Antonio-New Braunfels, TX Orlando-Kissimmee-Sanford, FL Sacramento-Roseville-Arden-Arcade, CA Cincinnati, OH-KY-IN Kansas City, MO-KS Las Vegas-Henderson-Paradise, NV Cleveland-Elyria, OH Columbus, OH Indianapolis-Carmel-Anderson, IN San Jose-Sunnyvale-Santa Clara, CA Austin-Round Rock, TX Nashville-Davidson-Murfreesboro-Franklin, TN Virginia Beach-Norfolk-Newport News, VA-NC Providence-Warwick, RI-MA Milwaukee-Waukesha-West Allis, WI Jacksonville, FL Memphis, TN-MS-AR Oklahoma City, OK Louisville/Jefferson County, KY-IN Richmond, VA New Orleans-Metairie, LA Raleigh, NC Hartford-West Hartford-East Hartford, CT Salt Lake City, UT Birmingham-Hoover, AL Buffalo-Cheektowaga-Niagara Falls, NY Rochester, NY Grand Rapids-Wyoming, MI Tucson, AZ Urban Honolulu, HI Tulsa, OK Fresno, CA Bridgeport-Stamford-Norwalk, CT Worcester, MA-CT Albuquerque, NM Omaha-Council Bluffs, NE-IA Albany-Schenectady-Troy, NY Bakersfield, CA Greenville-Anderson-Mauldin, SC New Haven-Milford, CT Knoxville, TN Oxnard-Thousand Oaks-Ventura, CA El Paso, TX McAllen-Edinburg-Mission, TX Allentown-Bethlehem-Easton, PA-NJ Baton Rouge, LA Dayton, OH Columbia, SC North Port-Sarasota-Bradenton, FL Greensboro-High Point, NC Little Rock-North Little Rock-Conway, AR APPENDIX: Seventy-Five Largest MSAs and Components (as defined February 2013)
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".