Bibliographic record
Abstract
This paper critically examines the evolving landscape of knowledge, skills and competence within university continuing education units, with a focus on the increasing atomization of lifelong education into micro‐credentials and other short-cycle learning models. While these trends respond to workforce demands for skills-first employment, particularly in unregulated fields, they also raise concerns about the authenticity and coherence of educational outcomes. Drawing on insights from cognitive science, the paper explores the dynamic processes of learning, emphasising the role of mental models (schemas and world models) in integrating and applying knowledge in novel contexts. The discussion highlights the challenges posed by semantic drift and inconsistent definitions of key educational terms, which complicate the alignment of educational outcomes with labour market needs. Furthermore, it critiques the limitations of micro‐credentials in fostering the broader, adaptive competencies required to navigate complexity and uncertainty. The paper argues for a holistic and integrative approach to lifelong learning, emphasising the social dimensions of education, clear definitions of skills and competencies, and authentic assessment practices. Ultimately, it advocates for reimagining educational frameworks to align with the biological and social realities of learning, ensuring that lifelong education achieves its transformative potential in an era defined by rapid technological and economic change.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.073 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".