MétaCan
Menu
Back to cohort
Record W2741163990 · doi:10.9782/2159-4341-20.2.90

Teaching and Supporting Students with Intellectual Disabilities in Democratic Republic of Congo

2017· article· en· W2741163990 on OpenAlexaff
Heather M. Aldersey, Shana J. Haines

Bibliographic record

VenueJournal of International Special Needs Education · 2017
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsCompassionPovertyDemocracyPersonhoodPassionPsychologyWork (physics)Intellectual disabilityPedagogySpecial educationMedical educationPolitical scienceMedicineSocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract Children with intellectual disabilities (ID) often experience challenges due to low learning expectations and educational quality and limited educational access. Often, in locations impacted by higher levels of poverty, such as Kinshasa, Democratic Republic of the Congo, educational disparities for children with ID are even more pronounced. To date, limited research has been conducted about educating children with ID from a teachers' perspective in Kinshasa. In this exploratory study, we analyzed data from 23 interviews with special education professionals who work with students with ID to describe their professional goals and explain the experience of delivering education to children with ID in Kinshasa. Most participants were drawn to their profession out of compassion and expressed the hope that they would help their students find jobs, live independently, and become valued members of society. The participants spoke in a uniform voice about how their passion and goals for their students were dampened by the difficulties they faced in educating their students, including low pay and the lack of a comprehensive system to support their work. Addressing these barriers is an important step towards ensuring the full personhood for all students in DRC, including children with ID.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
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 head0.035
GPT teacher head0.417
Teacher spread0.383 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of International Special Needs EducationSame topicFamily and Disability Support ResearchFrench-language works237,207