Teaching and Learning Within Inter-Institutional Spaces: An Example from a Community-Campus Partnership in Teacher Education
Bibliographic record
Abstract
This paper explores the potentiality inherent within a community-campus partnership in the area of inservice teacher education, and the inter-institutional space that has afforded creative and collaborative practices. Through this partnership, we endeavour to find innovative ways to better serve our students and create opportunities for smooth interactions and flow across school and university communities. Unlike other research that explores tensions and/or common ground within community-university partnerships, we seek to understand the potential that is created in the metaphorical space in-between institutions. Using dialogic inquiry, the diverse members of our teaching team, including members of the university community and the K-12 school system, as well as graduates of the program, reflected on the unique material, discursive and relational dimensions of our inter-institutional space. We came to see our graduate program as a hybrid place of connections, rhythms, and intersections in which usual institutional practices are ruptured. Together we identified powerful interrelated structural dimensions of our inter-institutionality, which we referred to as the gathering space, the inquiry space, the transformative space and the empowering space. These themes and the flow that has been created across and between institutions will be discussed in the following paper.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.020 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".