Tutorial para Futuras Cientistas: Um Relato de Cursos para a Disseminação da Participação de Mulheres na Área de Computação
Bibliographic record
Abstract
Vários são os estudos na literatura que apontam o público feminino como minoria nos cursos superiores da área de Tecnologia da Informação. Como consequência, inumerosas iniciativas surgiram para incentivar a entrada de mulheres para essas carreiras. Este artigo relata a elaboração de uma atividade de tutoriais, com um conjunto de minicursos estratégicos, na oitava edição do Encontro Acadêmico de Computação (EAComp), organizado pelo Programa de Educação Tutorial (PET) da Universidade Federal do Maranhão em 2021. O objetivo foi atrair o público feminino ao ingresso acadêmico no curso de Ciência da Computação. Também, este artigo apresenta a percepção das participantes sobre o impacto dessa atividade para o aumento e integração de mulheres no espaço acadêmico.
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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".