The Duplicitous Nature of the Familiar Urban Object: the Shopping Cart at Bennett’s Food Market, Where Food Buying is Most Satisfactory
Bibliographic record
Abstract
When Sylvan Goldman invented the first shopping cart in the 1930s, it is unlikely he envisioned its eventual entrance into the rivers and swamps. Though advertised as a solution for the arm-weary shopper, the function was no doubt two-fold in truth: while the explicit function of the shopping cart was to ease the load for supermarket shoppers, the more implicit function was to ease them into buying more. However, the customers at Bennett’s Food Market in Kingston, Ontario – at the corner of Charles and Bagot through the early 1900s to the early 2000s – helped to turn those expectations upside down. Through an extensive collection of oral history interviews, The Swamp Ward and Inner Harbour History Project has catalogued the neighbourhoods’ long-standing relationship with carts, but also the long-standing relationship with the grocery store that provided them. By focusing in on what first appears as a familiar urban object and considering it specifically in the context of Bennett’s Food Market, the shopping cart is revealed as far more than a basket on wheels. Shopping carts can nurture people not just by being filled up with food that’s then bought and consumed, and they can support a weight that’s not just of groceries, but that’s human.
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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 | 0.001 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".