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Record W4240378281 · doi:10.32920/ryerson.14646858

Is everyone using technology? : addressing the social and cultural needs of newcomer youth

2021· preprint· en· W4240378281 on OpenAlexaff
Laura Arndt

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMainstreamSettlement (finance)RealmPublic relationsService (business)The InternetSociologyPolitical scienceBusinessMarketing

Abstract

fetched live from OpenAlex

This mixed methodological study involving newcomer youth aged 17-24 and settlement service staff explores the role of technology in the process of settlement and the youth newcomer services. The findings indicate that youth prefer direct engagement with services and resources as they navigate the barriers that impact their access to mainstream and settlement service resources. They want to improve the quality of their engagement in communication as they strive to become part of their community. Participants involved in the study did not see technology as central to delivering youth settlement services. Youth did see the internet as necessary for schooling and in bridging to friends and family back home. The findings reflect the paucity of resources and capacity of mainstream and settlement specific services to meet the needs of youth, a lack of focus on integrated and accountable realm of settlement services specific to the needs of newcomer youth.

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 categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.009
Research integrity0.0000.001
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 head0.219
GPT teacher head0.368
Teacher spread0.149 · 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.

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
Published2021
Admission routes1
Has abstractyes

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