“Come and live in my Shoes”: Food Access and social isolation for People living in poverty IN GANANOQUE, Ontario
Bibliographic record
Abstract
This community-based research project, in collaboration with the Gananoque and Area Food Access Network (GAFAN), gathered data from self-reported food insecure residents of Gananoque and area to determine how to improve their access to healthy, personally acceptable food. In March 2016, I recruited 14 participants for three focus groups and one personal interview with those struggling to put food on the table for themselves and others in the household. Participants were single parents, adults over the age of 50, and adults who could benefit from improved access to healthy food but do not currently use existing services. Health issues, social isolation, scraping by, and lack of income were four themes that underscored the impact of poverty on the lives of participants. Lack of income, transportation, cost of food, lack of affordable or accessible childcare, and inadequate access to support services proved to be major barriers to food security: strongly influenced by the impact of rurality. The results of this research have the potential to help GAFAN improve food access for those living in this community. It may also have implications for enhancing food security in other rural Canadian communities.
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 | 0.001 | 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 | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".