The Evolution of Corporate Location Planning: A Survey Approach
Bibliographic record
Abstract
The unprecedented growth of big data has provided opportunities for the enhancement of retail location decision-making (RLDM) activities. Through a survey of Canadian retail location decision makers, this study examines the current state and progress in: (1) the type and scale of location decisions that retail firms undertake; and (2) the availability and use of geospatial big data and analytics within the decision-making process. The study finds significant increases in the usage of geospatial big data and analytics within corporate location planning. RLDM approaches have expanded to include new data sources, such as social media and mobile location data. With technology redefining consumption behaviors, the retail sector is looking to better understand how best to serve consumers in a market experiencing significant changes to the ways consumers shop. With granular level data being integrated into RLDM a skills gap is emerging in terms of handling and analyzing geospatial big data.
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How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.008 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".