Bibliographic record
Abstract
The numbers of street vendors around the world prove that they play a significant role in a vibrant and dynamic global economy. They are the ones who greet us around the world as we embark on our day-to-day journeys. And while some people appreciate the commodities and enticing cuisine they offer, not everyone perceives these vendors as a convenience. In many regions of the world, street vendors are viewed as destitute peoples who pollute public spaces and do not fit within the neoliberalist concept of a global city. \n \nWhat this synthesis map intends to do is bring to light how policy currently deals with street vending, look at street vending on public spaces and people’s perception of it, and finally make some suggestions as to how policymakers might approach this important issue. Through an analysis of academic papers, an online survey, and several Zoom interviews, I looked to gain a perspective on the matter. Having grown up around street vendors for a significant part of my early life, I also sought to inform my own bias towards them. Governments in the Global South know how difficult it is to design and implement a functional public space and mobility policy so hopefully, this content can provide some guidance. \n \nReading the map \nThe QR-Code on the map will take viewers to a page with further information, media and a report on the investigation. \n \nThe QR points to this page: \neduardoxaviergarcia.com/works/mobility-investigation/
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.156 | 0.023 |
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".