Bibliographic record
Abstract
Books Reviewed: Michael L. Lahr and Ronald E. Miller (eds.), Regional Science Perspectives in Economic Analysis: A Festschrift in Memory ofBenjamin H. Stevens Graham Clarke and Moss Madden (eds.), Regional Science in Business Luis Suarez–Villa, Invention and the Rise of Technocapitalism Sam Bass Warner, Jr., Greater Boston: Adapting Regional Traditions to the Present Jameson W. Doig, Empire on the Hudson: Entrepreneurial Vision and Political Power at the Port of New York Authority Alex Hirschfield and Kate Bowers (eds.), Mapping and Analysing Crime Data: Lessons from Research and Practice Aura Reggiani and Daniele Fabbri (eds.), Network Developments in Economic Spatial Systems: New Perspectives Joseph Persky and Wim Wiewel, When Corporations Leave Town: The Costs and Benefits of Metropolitan Job Sprawl Koichi Mera and Bertrand Renaud (eds.), Asia’s Financial Crisis and the Role of Real Estate Massimo Livi Bacci, The Population of Europe: A History Byron A. Miller, Geography and Social Movements: ComparingAntinuclear Activism in the Boston Area Rosalind Greenstein and Wim Wiewel (eds.), Urban–Suburban Interdependencies Heather Nicol and Greg Halseth (eds.), (Re)Developmentat the Urban Edge: Reflections on the Canadian Experience Marco Verweij, Transboundary Environmental Problems and Cultural Theory: The Protection of the Rhine and the Great Lakes Jonathan Raper, Multidimensional Geographic Information Science
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.577 | 0.594 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".