Bibliographic record
Abstract
The Encyclopedia of the Sciences of Learning is the first major reference work in the growing and highly visible field of learning sciences. It brings together definitional entries from scholars who represent the breath of this interdisciplinary area of study, teaching, and practice: biology, neuroscience, psychology, computer and information science, philosophy, anthropology, sociology, education, and a range of narrower technical and applied fields. Given the increasing amount of scholarly and popular attention to the dimensions and questions of human learning, it’s important to note that this encyclopedia devotes equal time to animal and machine learning as well; this is a significant difference that sets it apart from previously-published reference works on educational psychology. It also maintains an international sensibility in terms of contributors and topics and aims for an objective tone overall. Where it addresses specific institutions, politics and cultures of learning, it does so primarily to contextualize a specific learning behavior, process, method, theory, or concept.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.243 | 0.163 |
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".