Bibliographic record
Abstract
Community gardens are collaborative spaces where individuals work together to cultivate fresh, healthy, and affordable produce. Rooted in the broader practice of urban agriculture, which encompasses traditional farming, allotment gardens, and rooftop agriculture, community gardens are increasingly recognized for their role in addressing food security challenges, fostering social cohesion, and promoting ecological balance. Community gardening has become popular worldwide and received the attention of the scholars on its application to alleviate global food insecurity. Literature from across the world underscores their potential to alleviate food deserts in urban environments, as well as highlights significant disparities in the spatial distribution and accessibility of community gardens, raising concerns about equitable access to their benefits. Moreover, studies challenge their effectiveness as a standalone solution to food insecurity, emphasizing the need to align community garden programs with the specific needs and circumstances of low-income populations. Participation motivations, land-use planning, and long-term sustainability are additional critical factors shaping the success of these initiatives. To address these gaps, future research must focus on the design, governance, and stakeholder relationships that influence the overall effectiveness of community gardens in achieving food security and broader community goals.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".