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Record W4388759097 · doi:10.22582/ta.v12i2.674

Multi-Course and Faculty-Student Collaboration: Reflections on Implementing a Qualitative Research Project with Undergraduate Students

2023· article· en· W4388759097 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueTeaching Anthropology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTimelineCoronavirus disease 2019 (COVID-19)Work (physics)Medical educationPedagogyPsychologySociologyMathematics educationEngineeringMedicineGeography

Abstract

fetched live from OpenAlex

In this paper, we reflect on the development, integration, and implementation of a course-based, primary data collection fieldwork project for undergraduate anthropology students at the University of Guelph. Integrated across three courses taught between January-April 2022, we developed this project to provide students with the opportunity to build research skills and to broaden their understandings of how anthropological methods can be mobilized in timely, immediate ways, while at the same time engaging with diverse lived realities of the COVID-19 pandemic and lockdowns. We point to key factors that allowed for the success of this pedagogical experiment, which include established high levels of trust among involved faculty members; careful attention to timelines and organization; the distribution of project work among the faculty team; and choosing a topic that was timely, relevant, and engaging for students.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.509
GPT teacher head0.722
Teacher spread0.212 · 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