Bibliographic record
Abstract
This edited book, as you can see from its title, is about learning, or at least about the concept and practice of learning. The contributors to this volume are focusing on two meta-concepts, knowledge and learning, on the relationship between the two, and the way these can be framed in epistemic, social, political and economic terms. Knowledge and learning, as meta-concepts, are positioned in various networks or constellations of meaning, principally: the antecedents of the concepts, their relations to other relevant concepts, and the way the concepts are used in the lifeworld. In this book the various authors explore a number of important concepts that are relevant to the idea of learning. These are meta-concepts such as epistemology, inferential role semantics, phenomenology, rationality, thinking, hermeneutics, critical realism and pragmatism, and meso-concepts such as probability, woman, training, assessment, education, system, race, friendship, Bildung, curriculum, ecology and pedagogy. Like David Scott’s first volume of On Learning, this collection focusing on philosophy, concepts and practices is a response to empiricist and positivist conceptions of knowledge. It challenges detheorised and reductionist ideas of learning that have filtered through to the management of our schools, colleges and universities; over-simplified messages about learning, knowledge, curriculum and assessment; and fostered the denial that values are central to understanding how we live and how we should live – the normative dimension to social policy and social theorising. This book is also an attempt at a Bildungstheorie.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.233 | 0.093 |
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".