Elderly Walking Access to Street Markets in Chile: An Asset for Food Security in an Unequal Country
Bibliographic record
Abstract
Street markets can contribute to food security, since they are a source of fresh food and comparably inexpensive goods, being very relevant for low-income groups. Their relevance is even higher when considering older people, due to their often-constrained financial resources and possibilities to move. To assess the potential contribution of street markets to food security, this paper aims at evaluating to what extent older people have access to such a relevant asset. We consider the case of Chile, an ageing country with an unequal pension system, which makes it relevant for older people to access healthy and inexpensive food. We analyze what proportion of older people (i.e., people over 65) has walking access within 10 min to a street market—feria libre—in each Chilean region, with particular detail in the country’s four major urban areas. We compare the resulting accessibility maps with census data to identify neighborhoods with higher proportions of older people and examine their socio-economic conditions. Our findings show that while street markets are less accessible to older people in comparison to the general population, the inhabitants who can access them belong mainly to low-income groups. The results provide relevant insights to develop neighborhood-based policies for spreading and strengthening street markets, especially in low-income areas with insufficient levels of access to other relevant urban opportunities.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".