A spatial examination of Ohio’s economic growth
Bibliographic record
Abstract
It is widely acknowledged that Ohio’s economy involves distinct spatial patterns across difierent regions of the state. Despite this, data sources have not been available at a small enough spatial scale to al-low analysis of patterns of labor market change over time in urban, suburban and outlying areas. This study draws on a new database de-rived from establishment level (ES202) information on employment and earnings developed by the Ohio Urban Universities Program (UUP). The database allows us to examine growth of employment and earnings over the period from the flrst quarter of 1989 to the flrst quarter of 1998 for each zip-code area in the state. A major feature of the chang-ing economic landscape during the last decade has been the movement of flrms from urban to suburban and outlying locations. Our exami-nation analyzes these changing patterns of employment, earnings and establishments by classifying the 1,009 Ohio zip-code areas into urban, suburban and outlying areas. ⁄The authors would like to thank the Ohio Urban Universities Program for funding of the development of the ES202 data used in this study. 1 1
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".