Who gets left behind by left behind places?
Bibliographic record
Abstract
Data Information Database containing the Left Behind index and category for Census Designated Places (CDP) in the United States from the 2023 paper "Who gets left behind by left behind places?" The first 6 columns of the file "LBH_Places_Index.csv" contain geospatial join information to link the table to polygon files provided by NHGIS. The next three columns contain information on the place, name, and Census region of the CDP. Following the place information are columns containing the index rank (see Equation 1 of the paper) for 1980, 1990, 2000, 2010, and 2020. Finally, the last two columns contain the flags for if a CDP has changed left behindness (Enter, Exit, Both, None) and its current left behind category (Recently LB, Long-term LB, No longer LB, and Never LB). Index database derived from Opportunity Insights (Chetty et al. 2020) and IPUMS NHGIS (Manson et al. 2023). Main Text Connor, Dylan S., Aleksander K Berg, Tom Kemeny, and Peter J. Kedron. 2023. “Who Gets Left behind by Left behind Places?” Cambridge Journal of Regions, Economy and Society, September. https://doi.org/10.1093/cjres/rsad031. Acknowledgements We acknowledge the comments and support of our editors and anonymous reviewers at the Cambridge Journal of Regions, Economy and Society, participants at the CJRES online conference on ‘Left behind places and what can be done about them’, and the Spatial Analysis Research Center (SPARC) at Arizona State University. Partial funding has been provided through the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award numbers R21 HD098717-02. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Partial funding for this work was also provided through the Humans, Disasters and the Built Environment program of the National Science Foundation, award number 1924670. We also thank Lori Hunter, Johannes Uhl, Catherine Talbot, Andrés Rodríguez-Pose, Laura Tach, Rachel Franklin, Kevin McHugh, and audiences at the 2023 meetings of the Population Association of America and the American Association of Geographers, the Spatial Analysis and Data (SAD) Seminar, and the audience of the colloquium at the School of Geographical Sciences and Urban Planning at Arizona State University.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| 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) | 0.057 | 0.013 |
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".