The Quality of a ‘Local Values Based’ Fuctional Literacy Program: Its Contribution to the Improvement of the Learner’s Basic Competencies
Bibliographic record
Abstract
Literacy facilitators across the archipelago are currently faced with the challenge to integrate local values in functional literacy education programs, because its integration is a new aspect. This makes localized literacy content a hard thing to implement. Yet, if properly conducted, it can lead to improved learner’s basic competencies. In an effort to improve the learner’s competencies based on localized content, it is necessary to develop the facilitator’s competencies in regard to use of localized materials, as a measure to improve the quality of the learners. In this paper, the researcher aims to bring forward a model of functional literacy education based on local culture (values) for quality performance of the community learning centers within a learning society. The research reveals that literacy education based on local culture or local content can improve the quality of learning. The conclusion is that a model combining localized content, can enrich learning in this globalized world. Such a model can help learners to act locally, but with a globalized mind.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".