Understanding NUI-supported nomadic social places in a Brazilian health care facility
Bibliographic record
Abstract
We use the concept of third place as a lens to understand and catalogue the natural socializing practices of Brazilians within a chronic care hospital setting in order to understand natural practices that can be used on the design of NUI technologies to support Brazilian sociability and communities. Third places, as introduced by Oldenburg, are places that lie in-between the seriousness of work and the privateness of home, where social links are exercised through inclusive and playful conversation. We performed an ethnographic study with a community of Brazilian health care professionals at a chronic care hospital. We observed that daily socializing, through constant playful conversation creates a sense of togetherness that appears essential for problem solving and leads to more efficient work groups. We found that third places within the studied community happen as serendipitous gatherings where personal and work stories are exchanged. These gatherings occur in unexpected places and are nomadic in nature, thus, the third place location is fluid. NUIs and other ICTs can promote these gatherings by creating informational hubs where people can come together to acquire, discuss and share information.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".