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Record W4400966491 · doi:10.18060/27711

Intersecting Assets

2024· article· en· W4400966491 on OpenAlexaff
Brent Brodie, Shawna Teper, Byron Gray, Lorna Schwartzentruber

Bibliographic record

VenueMetropolitan Universities · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsYork University
Fundersnot available
KeywordsVendorPublic relationsApprenticeshipProcurementInstitutionReactionaryCommunity developmentBusinessCommunity engagementCommunity organizationMarketingSociologyPolitical scienceEconomic growthEconomicsPolitics

Abstract

fetched live from OpenAlex

This article investigates how strategic community partnerships form the bedrock of successful institutional community engagement activities. In this investigation, these engagements encourage institutional practitioners to consider how truly effective community economic development materializes when the university assumes a reactionary role by tailoring activity to respond to outcomes community defines for itself. Using the formation and development of York Unviersity’s successful social procurement program as test case, the article explores how key community partnerships have led to successful program outcomes – which, to date, have amounted to over $8million spent on diverse suppliers and 63 apprenticeship opportunities created. To demonstrate this evidence, the article considers how to identify and align with community champions to create tangible outcomes as well as how those defined outcomes are translated into creating activities that are aligned with what the institution can reasonably deliver to achieve the community’s stated outcomes. In this article, this will be considered as it explores York’s Social Procurement Vendor Portal and how its formation and refinement was directly attributed to community need. Finally, the investigation considers the interplay between how institutions can design impactful reporting that responds to community need.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.028
GPT teacher head0.356
Teacher spread0.328 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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
Published2024
Admission routes1
Has abstractyes

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