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Record W6912866418 · doi:10.5281/zenodo.832810

Review Of Host Group Service Models In Ontario: Citizenship And Immigration Canada

2005· article· en· W6912866418 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsHost (biology)CitizenshipImmigrationSettlement (finance)Service (business)Phone

Abstract

fetched live from OpenAlex

Citizenship and Immigration Canada’s Ontario Settlement Directorate (CIC) commissioned a review of Host group service models to identify which models were most promising, and to recommend changes in the implementation of Host groups to improve services to newcomers. The review was carried out in 2004/05, and made the following recommendations: 1. The core objective for Host groups should be defined as: To help newcomers link to diverse social networks in the host community. Other objectives, such as English practice, should be secondary. 2. Host groups should be designed to respond to newcomers’ priorities (e.g., practicing English, shopping, recreation, learning about Canada) while helping them to build social networks. 3. Host should emphasize social networks that link newcomers to the broader community. However, some newcomers may benefit from links with their own ethnocultural communities as a first step. 4. The most promising groups are characterized by the ways they are delivered rather than the service model itself. Of the nine Host group models described in the paper, all of them except the ‘Language Classes’ could be effective, but only if they are delivered using the following elements of promising programs: • Groups encourage extensive and in-depth informal conversations among both newcomers and volunteers, and about issues that matter to newcomers • Meetings are at community locations and public spaces where Canadians meet • Participants are encouraged to get together outside formal meetings (e.g., by exchanging phone numbers or within their school community) • Newcomers take on roles in the group where they can contribute and reciprocate (e.g., bringing potluck meals or welcoming newer arrivals) • Newcomers contribute to the host community (e.g., volunteering with community events or organizations) • Newcomers graduate from Host groups, either to sustainable natural groups or with the ability to use their informal ‘weak’ ties by contacting network members later • For conversation circles, most interaction is in small groups of 5 or under, and exercises promote acculturation and network-building. For example, structured activities like field trips could be designed to maximize social interaction and encourage informal linking afterwards, providing prolonged conversations in a real-life setting. Tutoring and homework clubs could promote community engagement if newcomer parents or older youth were trained to act as tutors, linking them to their communities in a way that developed their own skills and extended their networks. 5. Host groups should collect short-term indicators of success to ensure that they are addressing the Host mandate. Suggested indicators are: a. Number and type of social ties created through Host activities, both for newcomers and volunteers; b. Newcomer participation in community events and organizations (e.g., as volunteers) inside and outside their ethnocultural groups; c. Newcomer satisfaction with their social connections 6. CIC should support feedback systems to ensure that successful models are recognized and replicated and that useful practices are shared.

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)0.016
metaresearch head score (Gemma)0.040
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.156
Threshold uncertainty score0.979

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0140.043
Science and technology studies0.0040.003
Scholarly communication0.0070.002
Open science0.0040.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.259
Teacher spread0.215 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

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