La narrativa visual de una arteducadora bubi y gallega viaja a las aulas de sus dos orígenes
Bibliographic record
Abstract
Introduction: This article pretends to share the autobiography’s possibilities to explore the migrant reality, gender, race or the sense of belonging through the arts and how this research reaches the schools. Methodology: From the author’s life story —a black, Bubi (original people of the Bioko island, Equatorial Guinea) and Galician (Galicia, Spain) woman who inhabits a mostly white environment— an Art-based Research methodology is created involving visual memories extracted from the domestic photographic archive and microtales connected to racist experiences. Results: Connecting the privacy of that self-perception with an afro, common and collective discourse, the experiences are extrapolated completing them with artistic and bibliographical references. Then art educational strategies are created to show the students a decolonial, anti-racist and afrofeminist discourse to work with them about respect, self-knowledge and non-discrimination. Conclusions: The author’s journey as the daughter of a migrant woman tells her diasporic reality through the photograph, video, collage and identity narratives and it reaches class-rooms from different educational centres of Spain, Canada, India and Equatorial Guinea. Of them all, this article focuses on its implementation in her two origins: Oleiros (A Coruña, Galicia, Spain) and Ela Nguema (Malabo, Equatorial Guinea).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".