Equity and Accessibility in Last-Mile Crowdshipping Delivery
Bibliographic record
Abstract
This thesis explores sociotechnical innovations to improve equity, efficiency, and resilience in food rescue logistics last mile. Through qualitative inquiry and design science research, Chapter 2 introduces and pilots CrowdFeeding, a digital platform that enables direct donation to food delivery to clients. It presents a two-phase study conducted with 45 stakeholders throughout Canada. In the first phase, semi-structured interviews were used to identify key barriers. The second phase reports on a pilot study in Hamilton, Ontario, where a digital platform, CrowdFeeding, comprising a website and a mobile application was designed, developed and implemented to allow direct donor-to-client food delivery and address operational inefficiencies within food banks. Building on these insights, Chapter 3 introduces a three-sided market equilibrium model. It is developed to integrate volunteer deliveries into ridesharing platforms, demonstrating gains in driver earnings, platform profits, and environmental impact. The model incorporates regulatory constraints and behavioural tipping dynamics. Simulations using Manhattan-based data demonstrate reductions in food waste and CO_2 emissions, a 33% increase in driver earnings and a 10% increase in platform profits. Chapter 4 presents a unified optimization framework to strategize food bank operations that addresses donation procurement, purchasing produce, and equitable distribution of food. Numerical simulation showcase that the proposed policies improve efficiency, reduce costs, and reduce nutritional deprivation, outperforming heuristic approaches in most scenarios. The proposed model reduces procurement expenses by up to 40%, while an equity model cuts average deprivation by more than 50%. Finally, Chapter 5 offers future directions for scalable, data-driven and health-aligned food assistance systems. Collectively, this thesis offers a comprehensive, interdisciplinary foundation for reimagining non-profit food rescue system through digital innovation, participatory design, and operational rigour.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".