The Global Report on Adult Learning and Education (GRALE): Strengths, weaknesses and future directions
Bibliographic record
Abstract
Abstract One of the core outcomes of the Sixth International Conference on Adult Education (CONFINTEA VI) held in 2009 was the Belém Framework for Action (BFA). Its signatories committed to monitoring the most recent development stages of adult learning and education (ALE) worldwide on a regular basis, and to present and assess results in a global report. Coordinated by the UNESCO Institute for Lifelong Learning, surveys have been conducted and documented in four GRALE reports over the past decade. A fifth report is currently being prepared for CONFINTEA VII, to be held in June 2022. This article critically analyses the project of compiling a Global Report on Adult Learning and Education (GRALE) at roughly three-year intervals. Drawing on an evaluative framework for research quality developed by Pär Mårtensson and colleagues, the authors of this article investigate to what extent the GRALE approach to monitoring and reporting on ALE so far has been (1) credible (e.g. based on rigorous research methodologies and methods); (2) contributory (e.g. relevant and applicable to practice, generalisable); (3) communicable (e.g. accessible, understandable and readable in terms of report structure); and (4) conforming (e.g. with ethical standards). The purpose of this evaluation is for it to serve as a contribution to enhancing the quality of monitoring approaches in the field of ALE. This is vital for working towards future directions of ALE which are shaped by a high-quality evidence base. Ultimately, this will not only make ALE more accessible, fair, diverse and effective, but will also add to insights on how to achieve the Sustainable Development Goals in a similar way, especially since ALE indirectly but fundamentally affects the success of all 17 goals.
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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 | 0.202 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".