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Record W2743675765 · doi:10.7728/0202201104

Where in the World is My Community? It is Online and around the World according to Missionary Kids

2017· article· en· W2743675765 on OpenAlexafffund
Colleen Loomis

Bibliographic record

VenueGlobal Journal of Community Psychology Practice · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWilfrid Laurier University
FundersWilfrid Laurier University
KeywordsMedia studiesSociologyPsychology

Abstract

fetched live from OpenAlex

Having physical access to a community and having a sense of community is not always an easy option for Third Culture Kids (TCKs) who live in a culture other than their parents’ native cultures such as missionary families and government and non-governmental agency workers located in various countries around the world. One TCK stakeholder (a co-author) decided to practice creating community and research by conducting a participatory action research project with a goal of engaging a subgroup of TCKs called missionary kids (MKs) to meet online and to create a sense of community. Participants (N = 20) ages 16 to 40 joined website discussions and influenced how the website was developed and operated in addition to allowing their online postings to be used as data to study sense of community among MKs. Data were analyzed using McMillan and Chavis’s (1986) four dimensions of sense of community: membership, bi-directional influence, needs fulfillment, and shared emotional connection. Findings show that MKs connected through the Internet, developed a sense of community, influenced how the website functioned, took control of online community regulations and norms, and provided social support for one another. The website started in 2004 with two members and in 2011 had 1801 community members. Findings have implications for expanding theories of sense of community and for practices to create and sustain online communities.

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)0.017
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.172
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0120.000
Scholarly communication0.0000.001
Open science0.0040.002
Research integrity0.0000.013
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 head0.221
GPT teacher head0.573
Teacher spread0.351 · 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 designNot applicable
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

Citations4
Published2017
Admission routes2
Has abstractyes

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