Introduction to the Special Issue on Location Modeling
Bibliographic record
Abstract
The year 2009 marked the centennial of Alfred Weber's (1909) Theory of the Location of Industries, which more than any other single publication launched the field of Location Science.In the ensuing 100 years, the field has changed dramatically.The hand-drawn concentric circles and mechanical devices of the early 20th century were rendered obsolete by the invention of linear programming, digital computers, branch-and-bound, and GIS.On the computational side, simple but efficient greedy, substitution, and Lagrangean-relaxation heuristics have been augmented by newer developments in simulated annealing, genetic algorithms, ant colony optimization, Tabu search, heuristic concentration, as well as other approaches.Models for industrial factories and warehouses now occupy a small space in the field, having been joined by public sector facility location, emergency vehicle posting, special concerns for undesirable and obnoxious facilities, and countless other point, line, and areal facility problems.Researchers have explored different solution spaces (planar, network, discrete, spherical, and hybrid), numerous objectives (single vs. multiple), temporal aspects (static vs. dynamic), the nature of demand (point, planar, path flows), and other problem dimensions.Location researchers approach these issues from a variety of backgrounds: economics, engineering, geography, mathematics and operations research from both academic and business points of view.To bring these varied specialists together, Jonathan Halpern and Michael Goodchild, launched the International Symposium on Locational Decisions (ISOLDE) in 1978, which was held in Banff, Alberta.This symposium has been held every three years since 1978 for an intensive week focused solely on location decision-making.The papers in this special issue of Networks and Spatial Economics are expanded and refined treatments of topics identified at ISOLDE XI, held in Santa Barbara CA in 2008, as central to current scholarly inquiry into locational decisions.However, in no sense are
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.031 |
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".