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Record W73047894

How Do You Build A Community? Developing Community Capacity And Social Capital In An Urban Aboriginal Setting

2014· article· en· W73047894 on OpenAlexaffabout
Gus Hill, Martin Cooke

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsSocial capitalCommunity developmentMetisContext (archaeology)Public relationsEconomic growthSociologyDiversity (politics)Political scienceGeographySocial scienceEconomics
DOInot available

Abstract

fetched live from OpenAlex

Previous literature has identified social capital as an important resource for successful community development activities, and there have been some attempts to adapt the concepts of social capital to the particular context of First Nations. However, little information is available about how social capital itself might be developed or improved in Aboriginal communities. Moreover, urban Aboriginal communities are different from rural First Nations, Inuit or Métis communities in structure, composition, activities, and diversity, and deserve specific attention and their own models of community development. This paper presents a framework to guide development initiatives in urban Aboriginal contexts that is drawn from Aboriginal cultural principles and connected to the academic literature on development and social capital. Intended to provide practical advice to community leaders and practitioners, the framework includes five “tenets”: strategic planning; Elders and children; prayers and medicines; responsibility and ownership; and mentoring and role modelling.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.002
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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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 designNot applicable
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

Citations14
Published2014
Admission routes2
Has abstractyes

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