What’s Wrong with American Regional Science?
Bibliographic record
Abstract
* A version of this paper was given at a plenary session of the Canadian Regional Science Association in Victoria on M ay 31, 20 03. I app reciated v ery mu ch the q uestions and com ments that followed my presentation, and which were more generous than I had any right to expect. I am also grateful to SSHRC for fundin g the rese arch on w hich this p aper is b ased, to W. Isard and A. Scott for granting me interviews, and to S. Prudham and J. S eidl who provided logistical assistance and much m ore when I undertook archival work at respectively Cornell Univ & LSE. This paper overlaps in content with another paper of mine published in the Journal of Econo mic Geography, “The rise (and decline) of regional science: les sons for th e new e conom ic geography? ” The latter is concerned with using the history of regional science to think through the prospects of the new ec o no m ic geography, and represented especially by Paul Krugm an’s work. In contrast, this paper is concerned with making an argument about the decline of regional science based upon its universalist methodological position. * * Ed itor 's Note: It is appropriate that we start this first issue of the second quarter century of publication of the Canadian Journal of Regional Science with Trevor Barnes ' questioning of one of the conerns tones of Can adian regional scien ce. Becaus e of what Tre vor Barnes h as to say, I invited three colleagues to provid e their comm ents on his pa per. I hope this will encourage other readers to send in their own comments and analyses to our Dialogue section in subsequent issues.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".