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Record W1524980742 · doi:10.3968/5086

Accommodating Emotionally HIV/AIDS Children in the Classroom

2014· article· en· W1524980742 on OpenAlexvenueno aff
Maphetla Magdeline Machaba

Bibliographic record

VenueStudies in sociology of science · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHIV/AIDS Impact and Responses
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicPopulationAdversaryHuman immunodeficiency virus (HIV)PsychologyPolitical scienceEconomic growthCriminologySociologyMedicineCoronavirus disease 2019 (COVID-19)DemographyFamily medicineDiseaseComputer securityComputer science

Abstract

fetched live from OpenAlex

HIV/AIDS arrived on the world scene without warning. A few decades ago it was unknown lurking somewhere, waiting for the right moment to ambush the human race. Today HIV/AIDS covers Africa in dark clouds of fear, uncertainty and suffering. The virus has destroyed innocent hopes, desires and plans of countless numbers of people whose lives have been cut short by an unseen enemy. For those of us who live in Africa, it is a human catastrophe from which no single one of us in the region will be exempt, because HIV/AIDS affects us all. Using the qualitative approach, the study will recommend on how teachers can support the emotionally HIV/AIDS children in the classroom. This truism about the HIV/AIDS pandemic will become ever more evident and obvious as each month and year passes. The South African Gazette quotes alarming statistics proving that this pandemic in South Africa is among the most severe in the world and it continues to increase at an estimated rate of 33.8%. It is further estimated that almost 25% of the general population will be HIV positive by the year 2010. The outcome of the research will ensure that teachers and all the support structures contribute to ensure that infected and affected children in schools are cared for and supported according to their specific needs.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.011
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.323
Teacher spread0.262 · 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.

Study designObservational
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

Citations0
Published2014
Admission routes1
Has abstractyes

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