Bibliographic record
Abstract
Multicontext theory offers an approach to designing learning experiences and environments that take into account varied ways of thinking and knowing, are relevant inside and outside of the classroom, and can both enrich and encompass the lives of students on and off campus (Ibarra, 2001; 2005, Chavez & Longerbeam, 2016). Educators can leverage multicontext theory by integrating high context features like community wisdom, storytelling as knowledge, and inclusiveness into a traditionally low context system of experts sharing knowledge in a linear fashion to reap the benefits of both approaches (Chavez & Longerbeam, 2016; Weissmann et al., 2019). Examples of possible multicontext approaches are discussed, prompting readers to consider ways they can implement or may already be using multicontext teaching and learning. The classroom as a site of exposure to diversity is one of its fundamental gifts, and to make this more explicit through utilizing multicontext teaching and learning models is to enrich the learning environment, giving students more opportunities to communicate, collaborate, and learn.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".