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Record W2132330502 · doi:10.29173/css230

Conceptions of volunteerism among recent African Immigrants in Canada: Implications for democratic citizenship education

2012· article· en· W2132330502 on OpenAlexfundvenueaboutno aff
Ottilia Chareka, Joseph Nyemah, Angellar Manguvo

Bibliographic record

VenueCanadian Social Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDemocracyCivic engagementImmigrationCitizenshipSociologyInclusion (mineral)PoliticsCurriculumActive citizenshipPromotion (chess)Socioeconomic statusPolitical sciencePublic relationsSocial sciencePedagogyPopulationLaw

Abstract

fetched live from OpenAlex

In democratic societies the level of citizens ’ civic engagement and inclusion in all forms of democratic participation is crucial in maintaining social cohesion and a vibrant democracy. In the historical development of Canada’s demographic, political, socio-economic and cultural systems, immigration continues to play an influential role. Our paper presents conceptions of civic participation held by, inclusion and integration of recent African immigrants to Canada. We focus on volunteerism as one form of democratic participation. The findings show immigrants volunteer for the common good of society, making a difference, personal self-service gaining experience for advancement in their host society. Some are coerced into volunteering. Some of these findings concur with theoretical literature that positions various volunteering motives, bringing up implications for federal agencies involved in the settlement, adaptation programs for newcomers and educational curriculum planners attempting to widen conceptions of volunteerism, fostering engagement and promotion of citizenship education in general.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.435

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0330.017
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.053
GPT teacher head0.320
Teacher spread0.267 · 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 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

Citations8
Published2012
Admission routes3
Has abstractyes

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