Linking Theory and Practice in Hybrid Spaces through School-University Partnerships
Bibliographic record
Abstract
The purpose of this paper is to investigate the effects of our pedagogy designed to create hybrid spaces linking academic and practitioner knowledge within an integrated, school-based, and professionally-oriented practicum semester. Reforms to teacher education have called for extensive practical experience for teacher candidates and many of these calls have advocated for strong school-university partnerships as a means for increasing reciprocal collaboration in order to connect theory and practice. Attending to hybrid spaces, we designed a professional semester to include extensive practicum, school-based courses, seminars, and integrated assignments within the creation of authentic school-university partnerships. Qualitative data gathered included transcripts of six focus group conversations with administrators and teachers from individual schools, two joint meetings with administrators from four partner schools, and ten individual interviews with teacher candidates. Four themes were identified that related to the creation of a third space: co-constructing a common vision, negotiating alumni loyalties to other institutions, developing overlapping communities of practice, and creating new ways of integrating institutional requirements and partner desires. We conclude that the challenges we encountered in creating a third space and our attempts to create new knowledge as we resolved issues prompted robust opportunities for linking theory and practice.
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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 | 0.059 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.057 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.005 | 0.053 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".