EXTENSÃO UNIVERSITÁRIA EM TEMPOS DE ISOLAMENTO SOCIAL: DESAFIOS E ESTRATÉGIAS
Bibliographic record
Abstract
The university extension may provide the opportunity for dialogue and exchange of experiences between the university and the community. Thus, the study aimed to map the studies in Physical Education that problematize university extension in the social isolation to control the spread of the new Coronavirus (COVID-19), uncovering the challenges faced and the strategies adopted in the course of the projects. It is a State of the Question (SQ), by searching Google Scholar and in five Brazilian journals published in 2020 and 2021. It was found that there are university extension projects that have been occurring through virtual means, using tools such as websites, blogs and apps. The use of tools such as Google Classroom and interviews through Google Meet or other websites were considered quite promising for the continuation of the activities carried out by the university extension projects over the social isolation period.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".