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.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".