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Record W2058176053 · doi:10.3138/cmlr.67.4.568

Learning about HIV/AIDS in Uganda: Digital Resources and Language Learner Identities

2011· article· en· W2058176053 on OpenAlexvenueno aff
Bonny Norton, Shelley Jones, Daniel Ahimbisibwe

Bibliographic record

VenueCanadian Modern Language Review/ La Revue canadienne des langues vivantes · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLiteracySociologyPsychologyPedagogyMedicineMedical education

Abstract

fetched live from OpenAlex

While the HIV/AIDS epidemic has wrought havoc in the lives of millions of people in sub-Saharan Africa, access to information about the causes, symptoms, and treatment of the disease remains a challenge for many, and particularly for young people. This article reports on an action research study undertaken in a rural Ugandan village in 2006. Twelve English language learners, all of whom were young women, participated in this study. The focus was a digital literacy course that sought to help the participants gain access to information about HIV/AIDS through global health Web sites available in English, Uganda's official language. Our conceptual framework is drawn from theories of investment and imagined identities in the field of language education, and our central questions are twofold: (1) What were the learners’ investments in the language practices of the digital literacy course? and (2) What was the relationship between the learners’ investments in the course and their identities? Our findings suggest that the learners’ multiple investments in the digital literacy course derived not only from the significance of HIV/AIDS to their lives, but also from the opportunity to appropriate a range of imagined identities that offered enhanced possibilities for the future.

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)0.002
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 score0.402
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.308
Teacher spread0.280 · 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

Citations31
Published2011
Admission routes1
Has abstractyes

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