Scientific Approaches to Literature in Learning Environments
Bibliographic record
Abstract
Scientific Approaches to Literature in Learning Environments is not just about what takes place in literary classrooms. Settings do have a strong influence on student learning both directly and indirectly. These spaces may include the home, the workplace, science centers, libraries, that is, contexts that entail diverse social, physical, psychological, and pedagogical variables that facilitate learning, for example, by grouping desks in specific ways, utilizing audio, visual, and digital technologies. Scientific Approaches to Literature in Learning Environments puts together a series of empirical research studies on the different locations of teaching and learning. These studies represent literary learning environment throughout the world, including Brazil, the USA, China, Canada, Japan and several European countries such as the Netherlands, Ukraine, the UK and Malta. The studies reported describe quantitative and/or qualitative research and cover pre-primary, primary, high school, college, university, and lifelong learning environments. They refresh the enigmatic ambience that often surrounds the teaching and learning that goes on in literary studies and offer transparent, useful and replicable research and practice. Students and teachers alike are encouraged to take them and own them.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".