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Record W2326902632 · doi:10.1177/1937586716638101

Shared Opportunities on Institutional Lands

2016· article· en· W2326902632 on OpenAlex
Irena Knežević, Phil Mount, Chantal Clément

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHERD Health Environments Research & Design Journal · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsWilfrid Laurier UniversityCarleton University
Fundersnot available
KeywordsBusinessProduction (economics)Food processingHealthy foodHealth careEnvironmental planningHealth benefitsMarketingEnvironmental resource managementGeographyPolitical scienceEconomic growthMedicineEconomics

Abstract

fetched live from OpenAlex

AIM: This article outlines preliminary findings of a 3-year project that explored on-site food production on institutional properties, primarily healthcare facilities. BACKGROUND: There are growing pressures on healthcare facilities to improve their food offerings and incorporate food gardens into their health programs. While several healthcare facilities produce food on-site, there are few studies that explore opportunities, capacities, and institutional barriers related to on-site food production. METHODS: The study employed mixed methods including historical review, case studies, surveys, interviews, pilot garden projects, and Geographic Information System mapping. The number of participating institutions varied by method. RESULTS: Benefits associated with on-site food production can be health, economic, environmental, and social. There are also institutional barriers including administrative roadblocks, perceived obstacles, and the difficulty in quantitatively, measuring the qualitatively documented benefits. CONCLUSIONS: The benefits of food gardens far outweigh the challenges. On-site food production has tremendous potential to improve nutrition for staff and patients, offer healing spaces, better connect institutions with the communities in which they are located, and provide the long-professed benefits of gardening for all involved-from therapeutic benefits and outdoor physical activities to developing skills and social relationships in ways that few other activities do.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.339
GPT teacher head0.353
Teacher spread0.014 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it