(Re)searching (Trans-Multi)Culturally Responsive Curricular Conversations
Bibliographic record
Abstract
Haunted by the regurgitating moments of schooling experienced by myself and my students in multiple cultural contexts, in this paper, I attempt to initiate a provocative dialogue regarding the kinds of conversations we should have to bring “education” into today’s culturally diverse classrooms. By sharing the current dilemmas encountered by many students in contemporary schooling, specifically in science and mathematics classrooms, I argue for the creation and enactment of a (trans-multi)culturally responsive curriculum. Drawing on Aoki’s inspirited rhizomatic curriculum, Pinar’s currere as a complicated conversation, along with Schwab’s deliberated practice of engaging curricular commonplaces in a dialogue, I propose a (trans-multi)culturally responsive curricular framework as one way to invite teachers to engage deliberatively in a complicated conversation that could broaden their understandings of culturally responsive education in today’s classrooms.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.030 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".