Bibliographic record
Abstract
The term community is used extensively in the peer reviewed literature, though it is used differently by researchers across various disciplines. A better understanding of community, as an object of study, is needed to help guide policy, supports and services planning, and to build inclusive communities. This paper presents the results of a review of existing definitions published in peer-reviewed papers from various disciplines studying human behaviours and interactions. It also presents the results of focus groups with four persons with intellectual and developmental disabilities and members of their communities exploring their own definitions of community. Definitions of community extracted from the peer-reviewed literature were compared to identify common themes. Qualitative analysis revealed 13 themes, some more common than others. Focus groups transcripts were also analyzed. Themes identified in the literature review were also found in the focus groups discussion. However, a novel concept related to the notion of community as being composed of people who are unpaid to be part of this network was identified. Based on these results, a definition of community is derived to help further not only academic research in the area, but also to inform policy and practice aiming to build inclusive 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.014 | 0.030 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".