Why are you afraid of Indians: Issues of representation and misrepresentation in (Portuguese) children's literature
Bibliographic record
Abstract
Who hasn’t heard the phrase: “You look like an Indian”, to express that one is being naughty, or lazy, or violent or... something else, as long as it is negative? I have heard it a lot as a child and even nowadays, as a mother myself, I see/listen to (mis)representations when it comes to referring to First Nations – or indigenous peoples, if you will. In fact, before embarking on my research in Native Canadian literature for children and young adults and especially when I was about to depart to Canada to visit a reserve in Penticton and in Vernon, everyone around me took interest in what I was doing and told me I was crazy, as this seemed to be a dangerous field. In Portugal, songs and books for children still convey the image of the Indian as a merciless warrior who is ready to scalp you and peel your skin off. That is the reason why, after studying Native Canadian literature for children and young adults, I believe it is important to analyse the way that the Indian is still portrayed in Portuguese literature, in particular through a brief reading and discussion of Maria Teresa Maia Gonzalez’s A História dos Brincos de Penas, a book that is recommended by the Portuguese National Reading Panel. Thus, this study combines postcolonial theory and literary criticism to discuss issues of representation and misrepresentation, ultimately leading us to understand the importance of Bhabha’s third space, a space where positive negotiations and renegotiations give rise to hybridity, a space where one does no longer need to be afraid of Indians.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.029 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".