Where in the World is My Community? It is Online and around the World according to Missionary Kids
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".