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Record W2561660068 · doi:10.22582/ta.v6i0.432

The Disorienting Dilemma in Teaching Introductory Anthropology

2016· article· en· W2561660068 on OpenAlexaff
T. F. McIlwraith

Bibliographic record

VenueTeaching Anthropology · 2016
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPoxvirus research and outbreaks
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTransformative learningDilemmaSociologyEpistemologyField (mathematics)PedagogyPsychologyPhilosophyMathematics

Abstract

fetched live from OpenAlex

This paper uses Jack Mezirow’s concept of the disorienting dilemma to discuss opportunities in anthropological teaching to transform student beliefs. It compares the connections between classroom instruction in cultural relativity, a core concept in cultural anthropology, and field-based anthropology experiences related to the same concept. Drawing on examples from my classroom and from a research-oriented field school, my observations suggest that while students are good at understanding cultural relativity intellectually, and identify or define the concept easily on tests, they are not as capable at applying the concept to observations made of films or in field settings, situations which are disorienting for students despite the fact they have the conceptual tools to work through them. Further, the paper asks if trigger warnings and disorienting dilemmas are actually the same thing, wondering too if trigger warnings are consistent with the transformative potential of higher education promoted by Mezirow.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0070.037
Scholarly communication0.0100.016
Open science0.0020.012
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.014
GPT teacher head0.309
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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