MétaCan
Menu
Back to cohort
Record W2803477708

Public sector purchasers as curators and value creators in the food system.

2015· other· en· W2803477708 on OpenAlexaboutno aff
Hayley Lapalme

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2015
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementMarketingPurchasingPublic relationsTransformative learningBusinessPurchasing powerLeverage (statistics)Food systemsPovertyFood securitySociologyPolitical scienceEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

The 3P Mentorship Program is a community of practice that
\nconvenes institutional food buyers around a shared vision to use
\nthe $750 million purchasing power of the Ontario public sector
\nto foster resilient local food systems. Five design principles
\nemerged from the program, which ran as a pilot in 2014-2015
\nwith a cohort of four institutional mentees: a hospital,
\nuniversity, college, and long term care home, each represented
\nby a manager influencing the institutions’ procurement. System
\nmapping and informal interviews revealed that the point of
\npurchase was a high leverage, low friction point of intervention
\nwhere procurement mechanisms, such as the RFP, make
\ninstitutions passive consumers of value from the food system. A
\nchallenge emerged to design a minimally disruptive intervention
\nthat would enable managers to re-claim these mechanisms and
\nto re-imagine their institutions as creators of value, in a position
\nto curate the “reconfiguration of roles and relationships among
\n[the] constellation of actors” for a more resilient food system
\n(Normann and Ramirez, 1993). The pilot generated evidence of
\nthe ability of networked institutions to collaborate on a shared
\nvision to increase the social good generated through
\npurchasing, and to play a transformative role in food systems.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.851
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.069
GPT teacher head0.240
Teacher spread0.171 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueOCAD University Open Research Repository (OCAD University)Same topicOrganic Food and AgricultureFrench-language works237,207