Networked Knowledge: Cultural Sharing Amongst Dispersed Immigrants
Bibliographic record
Abstract
Global immigration and population displacement are happening now at rates higher than ever before in modern society. There is a compelling opportunity to take advantage of networked technologies to preserve cultural identity in the face of immigration while addressing problems of cultural integration. Personal devices such as cell-phones and laptops that let us connect to the internet and one another are now widely affordable and available. There is potential here that one might exploit by sharing a network of knowledge that brings immigrant populations in touch with one another and with the culture of their new “chosen” homeland. This document presents a design research-based approach to possible future explorations in the field of service design that promotes culture preservation. It explores how a personally accessible mobile application can help to create and more importantly, visualize a network of peers one can depends on for culturally relevant information. The application was co-designed via a collaborative workshop with members of PICS: Progressive Intercultural Community Services, Surrey (British Columbia), a non-governmental organization that has been serving the community since 1987. The article also explores how building a virtual community can be the node to forming real-life communities and aid in cultural integration for recent Indian immigrants to Vancouver. Furthermore, the article proposes subjective solutions and their implications for a future mindful globalization.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".