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Record W2555391043 · doi:10.38140/pie.v33i4.1929

From discomfort to collaboration: Teachers screening cellphilms in a rural South African school

2015· article· en· W2555391043 on OpenAlexafffund
Katie MacEntee, April Mandrona

Bibliographic record

VenuePerspectives in Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNegotiationHuman immunodeficiency virus (HIV)Focus groupPandemicWork (physics)Teacher educationPedagogyPsychologySociologyMedical educationMedicineCoronavirus disease 2019 (COVID-19)Family medicineSocial science

Abstract

fetched live from OpenAlex

South Africa continues to contend with an HIV pandemic. Teachers have the potential to address prevention and treatment with their learners but they struggle to implement HIV and AIDS education. Cellphilm projects—using cellphones to create videos, and then screening these—is one example of how digital technology can be used to address barriers to teacher-implemented HIV education. In this article we focus on the work of nine teachers who screened their cellphilms to three youth audiences. We explore how teachers can integrate cellphilm screenings into their teaching practice to address HIV and AIDS, and we consider what this integration tells us about the potential and challenges of teachers dealing with this issue in rural South Africa. Informed by a framework of discomfort, we analyse participant observation notes, fieldnotes, and pre- and post-event interviews. We argue that moments of discomfort during the events reveal the difficulties and strategies that teachers use to negotiate multiple—sometimes contradictory—sexual health education policies. The cellphilm screening events provided an opportunity for teachers and youth to learn from each other, even as it contributed to a more nuanced response to the teaching that addresses HIV and AIDS.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.102
GPT teacher head0.467
Teacher spread0.365 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations17
Published2015
Admission routes2
Has abstractyes

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