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Record W2170106797 · doi:10.1504/ijpm.2011.040372

Supply chain integration under chaotic conditions: not-for-profit food distribution

2011· article· en· W2170106797 on OpenAlex
Paul D. Larson, Ron McLachlin

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Procurement Management · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBusinessSupply chainMarketingSupply chain managementProfit (economics)Work (physics)Food supplyIndustrial organizationEconomics

Abstract

fetched live from OpenAlex

This article describes and discusses unique supply chain integration challenges faced by not-for-profit (NFP) organisations, as opposed to for-profit businesses. In addition, the article covers possible transfer of ‘best-practices’ from business logistics/supply chain management (SCM) to the NFP sector. Using mostly qualitative methods, the study focuses on Winnipeg Harvest, a NFP organisation that provides food to people struggling to feed themselves and their families. Unlike businesses, NFP organisations rely on volunteer labour and they target social (rather than economic) objectives. They also work with and serve a wider range of stakeholders compared to the for-profit sector. This article is among very few to focus on unique supply chain/logistics challenges of the NFP sector. Its value is in inspiring additional research in NFP logistics. Furthermore, and more importantly, it might help NFP organisations serve more people in need – and serve them better.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.796
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.254
Teacher spread0.217 · 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