MétaCan
Menu
Back to cohort
Record W4234340290 · doi:10.24124/2016/1230

Literacy: a new discourse for effective change

2016· dissertation· en· W4234340290 on OpenAlexaff
Erin Grace Evans

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsPhotovoiceLiteracyContext (archaeology)Qualitative researchImmigrationPsychologyFamily literacyPedagogySociologySocial sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

The purpose of this research is to understand the link between the acquisition of literacy skills and change in a Northern context. Furthermore, this research explores the perceived role that improved literacy plays in helping individuals make change. A literature review highlights the history of literacy in the community, First Nations and immigrant literacy, social implications of low literacy and community indicators. My research sample consisted of seven participants living in a northern community in British Columbia. The approach was qualitative and utilized an adapted photovoice method. The data was pre-coded, then analyzed using descriptive coding and finally pattern coding. The research findings revealed four themes: (1) continuous learning; (2) enhanced self-confidence; (3) increased opportunities; (4) connecting with self and others. The findings also showed that with improved literacy the participants were able to make positive, lasting changes in their personal and professional lives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0130.082
Scholarly communication0.0220.024
Open science0.0020.013
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0060.001

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 head0.615
GPT teacher head0.735
Teacher spread0.120 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicParticipatory Visual Research MethodsFrench-language works237,207