A commentary on community living: Finding and creating a sense of community
Bibliographic record
Abstract
A sense of community is very important to an individual, as it helps him/her maintain a sense of belongingness and benefit from the availability of social support. While there is a great deal of research regarding how communities are created and maintained, there is still much more to be done. This article describes an informal investigation into peoples’ understanding of community, how they found their social niches, and discussions of the similarities found between different interviewees. The initial approach was to compare and contrast two very different cities: Boston and New York, highlighting the impact the environment has on the ability of one to create his/her social group. However, what was found through conversation was that community is less about the location one is embedded in, and more about lifestyle choices.
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 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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.060 | 0.099 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".