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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".