DOI:10.1068/a3562 Behind the web store: the organisational and spatial evolution of multichannel retailing in Toronto
Bibliographic record
Abstract
Abstract. In this paper I address two issues of general relevance to contemporary debates in economic geography: first, the organisational and spatial implications of new information technologies for the economic landscape; and, second, the enduring role of place to digital capitalism. Specifically, I examine the organisational evolution of multichannel retailing in Toronto from a geographical perspective. Bricks-and-mortar retailers are increasingly pursuing a multichannel strategy by operating an Internet-based web store alongside the existing network of physical retail outlets. I therefore evaluate the organisational implications of the adoption of business-to-consumer e-commerce (e-tailing) technology for six Canadian bricks-and-mortar retailers based in Toronto and assess how the associated changes in business structure have been inscribed upon the urban landscape. The argument is developed in three sections. First, I discuss how the formula for competitive advantage in the new (r)etail markets of the developed world has shifted from a pure play to a multichannel organisational paradigm. Second, I provide a background to the development of Canadian e-commerce and an overview of the empirical methodologies employed during the research. Third, the focus of the paper moves `behind the web store ' to spatialise the physical places that constitute the fulfilment infrastructure of e-tailing as sequentially linked stages in Internet
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.013 |
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".