Illuminating Meshworks of Pre-Service Teachers’ Curated Co-Living Learning Spaces
Bibliographic record
Abstract
Learning spaces in higher education are fraught with colonial barriers such as teacher-centered, front facing, stark, feelingless, and unwelcoming classrooms that diminish students’ feelings of well-being. For pre-service teachers, these are also the types of classrooms that they often inherit as they foray into the profession. Three Bachelor of Education (B.Ed) assistant professors investigate how pre-service teachers’ well-being shifted when collectively (re)imagining and (re)envisioning a colonial university classroom space in a faculty building that is over 100 years old. They then share the findings of their a/r/tography, action research inquiry that captured the co-living, metabolic experiences and relational meshworks of both participants (n=11) and researchers documented through reflexive journaling, artistic artifacts, interviews (n=3), and contemplation. The researchers embody decolonizing praxes through intentional interpretation and writing scholarship as they weave their storied inquiry. They conclude with transformative urgencies for how B.Ed programs can recalibrate their physical learning spaces to better support and sustain teachers’ well-being in their future profession.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.046 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".