Recent Immigrants as an "Alternate Civic Core". How VCN Provides Internet Services and Canadian Experiences
Bibliographic record
Abstract
Vancouver Community Network (VCN) is a charitable internet service provider offering opportunities to contribute to VCN’s operations in a volunteer capacity. Nearly all of VCN’s volunteers are information and communication technology (ICT) professionals — or students with career goals in that field — and more than 60% have immigrated to Canada in the past five years. As newcomer-volunteers search for full-time employment commensurate with their skills, they volunteer as Technical Help Desk Support, Internet Instructors, Local Area Network Support, or Language Portal Developers. By doing so, newcomers interact with one another and with VCN’s members in ways that increase social capital and contribute to social inclusion. Assisting in the network’s mandate of providing opportunities for online participation creates openings for volunteers to meet face-to-face, share information, and work with network members from diverse cultural backgrounds and varied socio-economic circumstances. While it is the volunteers’ own efforts and initiative that bring them to VCN, their collective contributions are important to the success of VCN’s internet service provision and additional member services. Working toward these goals allows newcomers to experience civic participation and community-oriented learning, particularly in relation to ICT work skills. Based on qualitative and quantitative research, this is an examination of how human and social capital is built at VCN, and how it contributes to social inclusion and integration for immigrant volunteers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".