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Record W2720334859

The Nonprofit Sector Capital Market in BC and Alberta

2009· article· en· W2720334859 on OpenAlexaboutno aff
Rebecca Pearson, Jonathan Lawrence

Bibliographic record

VenueAUSpace (Athabasca University) · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessFinancial capitalIndividual capitalSocial capitalFinanceTriple bottom lineCapital marketCapital (architecture)Nonprofit sectorProfit (economics)EconomicsSustainabilityPublic relations
DOInot available

Abstract

fetched live from OpenAlex

A nonprofit capital market that invests in triple bottom line returns is emerging in Canada. Stemming from individuals, foundations, and governments providing donations and grants to nonprofit organisations to support social and environmental services, the nonprofit capital market is growing and innovating, bringing the rigours of the business world to bear in maximising social impacts and leveraging available nonprofit capital in more effective ways. Because investors are demanding a triple bottom line return instead of simply financial profit maximisation, the nonprofit capital market frees up organisations to pursue a social and/or environmental return that is not constantly traded off with the need to maximise financial profits. The nonprofit capital market can be viewed as a model or at least a starting place for understanding a new kind of capital market for all ventures that would flow resources to projects that create sustainable wealth.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0060.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.015
GPT teacher head0.184
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2009
Admission routes1
Has abstractyes

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