The art and science of retail location decisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although formal techniques of locational analysis have been available for over 50 years, most retailers traditionally made no use of them, relying instead on intuition guided by experience and “common sense”. However, the simultaneous advent in the last 15 years of low cost computing and the increasing availability of retail related data of all types has given retailers the opportunity to take a much more rational approach to decision making. This paper examines the extent to which retailers have taken advantage of the potential released by these developments, and adopted more “scientific” rules based methodologies. The analysis is based on an extensive questionnaire survey of UK retailers conducted in 1998 which encompassed organisations operating altogether more than 50,000 outlets across eight sectors. The survey sought to identify the use made both of particular types of techniques, and of Geographical Information Systems, which act as a platform for them. It was complemented by a series of in‐depth interviews with location specialists in a number of major retail organisations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it