Playing with Names: How Children Create Identities of Self in Anthropological Research
Bibliographic record
Abstract
Children have long been present in anthropologi cal research but rarely does their involvement receive the criti cal attention that is given to adult participants. This paper examines what is revealed about the construction of identities of self when the research-related play of two girls in quite dif ferent ethnographic contexts is examined. Drawing on research in Bridgetown, Barbados, and Saskatoon, Canada, it is argued that through the playful search for a pseudonym, these two girls speak to issues of poverty and prostitution that highlight how they see, experience and contribute to the world around them as active agents, rather than passive recipients, of cul tural processes. are to be seen and not heard resonates today through out the many academic studies of global poverty, social adversity and personal despair. Heightening the visibility of children and persuading readers of the significance of the issue being discussed, images of children often emerge
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".