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Record W1585713430

TACKLING POVERTY THROUGH HOLISTIC, INTERCONNECTED, NEIGHBOURHOOD-BASED INTERGENERATIONAL LEARNING: THE CASE OF WINNIPEG’S SELKIRK AVENUE

2015· article· en· W1585713430 on OpenAlexaffabout
Shauna MacKinnon, Jim Silver

Bibliographic record

VenueUniversitas Forum · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsPovertyDistrustNeighbourhood (mathematics)Context (archaeology)SociologySocial capitalEconomic growthColonialismCommunity educationPolitical sciencePedagogyGeographySocial scienceLawEconomics
DOInot available

Abstract

fetched live from OpenAlex

Winnipeg is the capital city in the Province of Manitoba, Canada. It is home to a high proportion of Aboriginal people, many of whom live far below the poverty line and drop out of school at an early age. Many have lived in poverty for generations and have little hope of escaping it. The reasons are in part attributable to colonial policies that have left a legacy of despair and distrust, particularly in the education system.  Community-based organizations, post-secondary education institutions, governments and others are working in collaboration to build a holistic education model that provides opportunities for Aboriginal people and other multi-barriered residents through a cradle to college approach. Programs recognize the damaging effects of colonization and integrate decolonizing pedagogical methods. Recently the community acquired a century old property, the Merchants Hotel, which had become a magnet for violence and many serious social problems. The community’s vision is to reclaim this space as a multi-faceted place of learning that will further connect and expand upon existing educational initiatives. This paper and video describe the historical context, our pedagogical approach and what we have learned to date as we move forward with the development of an intergenerational community campus.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.506

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.043
GPT teacher head0.311
Teacher spread0.268 · 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 designQualitative
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

Citations3
Published2015
Admission routes2
Has abstractyes

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