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Record W4211257728 · doi:10.1080/14480220.2022.2032268

Measuring adult English literacy improvements in First Nations communities in Australia

2022· article· en· W4211257728 on OpenAlexaboutno aff
Bob Boughton, Frances Williamson, Sophia Lin, Richard Taylor, Jack Beetson, Ben Bartlett, Pat Anderson, Stephen Morrell

Bibliographic record

VenueInternational Journal of Training Research · 2022
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsLiteracyGovernment (linguistics)Vocational educationProject commissioningAdult literacyMedical educationPublishingEconomic growthPolitical sciencePsychologyPedagogyMedicineEconomics

Abstract

fetched live from OpenAlex

The prevalence of low to very low adult English literacy levels in First Nations communities in Australia continues to be an issue, despite ten years of government-supported Foundation Skills training provided through the national vocational education and training system. This study examines an innovative First Nations community-controlled approach to improving adult literacy training, utilising an internationally recognised mass campaign model. Literacy improvements were assessed for 63 participants in 6 communities, using validated pre- and post-tests aligned to the Australian Core Skills Framework (ACSF). Overall, 73% of participants improved their literacy, defined as moving up at least one level on one or more of six ACSF indicators. The number of lessons completed and entry ACSF literacy levels were significantly associated with literacy progression, with previous school education positively associated but not statistically significant. The minimum number of lessons associated with literacy improvement is estimated as 47–49 (80–83% of lessons).

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.225
GPT teacher head0.459
Teacher spread0.234 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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