Relational Logistics and Alternative Supply Chains of Last Resort
Bibliographic record
Abstract
This paper contrasts the consolidation of the American mega-logistics industry with homegrown logistics operations based in neighborhoods of New York City during the COVID-19 pandemic. We offer three cases: a community of immigrant street vendors in Corona, Queens; a theater-turned-food-pantry in the Lower East Side of Manhattan; and the city-wide network of mutual aid organizations. We note the ways these alternative forms of logistics stitch together technologies, delivery processes, and social networks-often quite creatively-to get food and other resources where they need to be, when they needs to be there. These logistics networks are locally-oriented, fit to specific requirements, and made up of tailored, ad hoc activities. They demonstrate flexibility and skillful work in a crisis and bring together volunteers, entrepreneurs, and necessary resources with a community-at-need in horizontal governance. In doing so, they model forms of supply chains that improve social welfare and offer alternatives to the growing consolidation of mega logistics. We identify opportunites for CSCW scholars to use insights from the field including theories of infrastructure and technology and social movements to support the further development of such supply chains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".