Images of the Other in Childhood: Researching the Limits of Cultural Diversity in Education from the Standpoint of New Anthropological Methodologies
Bibliographic record
Abstract
This paper presents the justification, methodology, main results and pedagogical implications of a study on how children represent others, carried out with primary school children in the Madrid Autonomous Community. Based on a methodological design suggested by the new ways Cultural studies and Visual anthropology provide for approaching reality, we have tried to answer the question, “What do these children see in the images of those who are culturally different?” One of the results of the study indicates how cultural differences such as customs and forms of dress outweigh physical differences such as skin color in the representations the children made of others. Most of all, the results reveal the great richness of detail the children saw hidden behind the images of others. We should take steps so that the current education system’s efforts to promote tolerance and recognition do not drown that rich and varied detail in conceptions of cultural diversity that are too narrow and unyielding. Now more than ever, cultures ought not be seen as closed units that build walls and unsavable limits between themselves, but as sets of interacting trends. Educating in a multicultural environment thus means teaching to see the relativity and artificiality of cultural borders, helping to find the “you” living in the other, the particular biography superseding all tags and labels.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".