International assessment of low reading proficiency in the adult population: A question of components or lower rungs?
Bibliographic record
Abstract
Abstract Among the United Nations’ 17 Sustainable Development Goals (SDGs) launched in 2015, the fourth goal (SDG 4) is dedicated to education, and one of the ten targets within that goal specifically addresses adult literacy and numeracy skills. Efforts to reach this target involve monitoring, which in turn involves assessment. The most powerful instrument for assessing literacy proficiency is the Programme for the International Assessment of Adult Competencies (PIAAC), conducted by the Organisation for Economic Co-operation and Development (OECD). It has five hierarchically organised proficiency levels for literacy. A sixth category, labelled “below Level 1”, lumps together low proficiencies at the bottom end of the proficiency continuum. To boost effective action in addressing SDG 4, the UNESCO Institute for Statistics (UIS) recently launched the Global Alliance to Monitor Learning (GAML), which aims to support national assessment strategies and to develop internationally comparable indicators and methodological measurement tools. While PIAAC Levels 1–5 are already broadly suitable for international comparison, the “below Level 1” category has so far only been assessed by individual countries (e.g. Canada, the United States, the United Kingdom and Germany) using instruments developed nationally. Focusing on the reading aspect of literacy, the authors of this article investigate how these nationally developed low proficiency assessment instruments might be adjusted to facilitate international comparability.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".