Educação visual sobre o Holocausto: : sensibilização e ética na utilização de imagens históricas
Bibliographic record
Abstract
For a long time, research in Holocaust education focused on the transmission of historical facts and the preservation of collective memory. However, there is a significant gap concerning the ethical and contextualized use of images, especially those generated by artificial intelligence, in teaching this sensitive topic. To explore this issue, this article analyzes the importance of contextualization and the ethical use of images in Holocaust education, emphasizing the responsibility of educators to select visual materials that respect the dignity of the victims and promote a critical understanding of historical events. Based on the guidelines of the Brazilian Association for History Education and the principles of the International Holocaust Remembrance Alliance (IHRA), the study conducts a qualitative analysis of pedagogical practices in scientific articles focusing on the integration of images into the curriculum and identifying gaps in the National Common Curricular Base (BNCC) related to the Holocaust. The results indicated that while technology can enhance education, the lack of contextualization and critical reflection in the use of images may compromise the integrity of the educational process. As its main contribution, the article highlights the need for an ethical and careful approach to the use of images in Holocaust education and suggests the inclusion of explicit references to the subject in the BNCC, aiming to strengthen students' critical and empathetic formation.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".