Between the Test and the Deep Blue Sea: Sneaking Anthropology into the Classroom in Post- “No Child Left Behind” America
Bibliographic record
Abstract
Since the implementation of the No Child Left Behind Act of 2001 (2002), K-12 teachers in the United States (US) have operated under a legal regime of standardised testing, designed to measure student progress on pre-determined metrics. However, many teachers, and their students, find this approach stifling and inconsistent with the student-centred, Freirean liberation pedagogy approach they often encounter in teacher training programmes. This piece looks at the tension between educational policy and teaching philosophy. Through my own experiences as a high school social studies teacher in the US, I demonstrate how I used ethnographic methods in my classroom to foster an anthropological sensibility. I focus on one specific project – the Snapshot Migration Story – and how I used it to carve a space for ethnographic exploration, student-centred curriculum, and project-based learning. This anthropological sensibility provided “windows and mirrors” to the students, helped “make the familiar strange and the strange familiar,” and helped students navigate an educational landscape that tried to both create lifelong, passionate learners, as well as students with the ability to reliably and consistently pass standardised tests.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.047 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.011 |
| 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".