The last meter problem in cities: digital platforms, urban signage, and the enduring analog
Bibliographic record
Abstract
Digital platforms are premised on the frictionless brokering of access to urban goods, services, and amenities over the distance of the “last mile” in cities. Yet platforms’ purported capacities for coordinating urban space-times for platforms frequently lose prowess over the final, crucial spatiality of the “last meter” where rides are hailed, meals are delivered on-demand, and shared bikes and e-scooters are parked. Resolving the last meter problem in cities necessitates analog spatial interventions in the built environment to close logistical and mobility gaps left open by insufficiencies of algorithmic management. Drawing on the results of a geosemiotic analysis of a selection of primary image data, we identify one such analog intervention that makes digital platform operations function as intended: urban signage. Signs emplaced in the cityscape outside of restaurants, at the door of residential buildings, or on a sidewalk maintain interaction order between human actors engaged in digitally-brokered transactions, enabling a hailed ride, the delivered meal, and micromobility stationment to actually take place in urban space. We connect these findings to the heterogeneity of cities and the messiness of digital fulfilment, demonstrating the limits to algorithmic management of goods and services and attesting to the enduring salience of the analog.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".