Measuring the impact of literacy programs on social inclusion, health and labour market participation: A study of a Dutch program
Bibliographic record
Abstract
Nowadays, 24.5% of the European population is still at risk of poverty and can be considered as citizens risking social exclusion. A low level of proficiency in literacy skills is indicated as one of the important reasons for social exclusion. It is argued that literacy programs for vulnerable adults act as a lever for the improvement of literacy and, in turn, for enhancing participants' social inclusion, health, as well as labour market position. However, to date, evidence of the impact of these programs is scarce. This study aims to fill this gap by measuring the outcomes of the Dutch program 'Language for Life' ('Taal voor het Leven'). The findings indicate that after five months, for many social inclusion indicators, more than half of the participants' group showed an increase. Improvement in physical and psychological health and labour market position is less prominent than improvement in the social inclusion indicators. This study does not only support the importance of a policy in literacy (on the national or regional level) aiming at increasing social inclusion by offering possibilities to improve language proficiency. Moreover, it aims to contribute to the investment in research aiming to monitor the outcomes of language programs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".