Place as Teacher: Community-Based Experiences, Third Spaces, & Teacher Education
Bibliographic record
Abstract
This study focuses on the systematic collective reflections of four teacher educators as they interrogate their own practice engaging in community-based settings, specifically considering how these settings can serve as an additional teacher. It is informed by a theory that centers educational experiences within the community and is guided by Gutiérrez’s use of the concept of third space. Connected to third spaces, the results of this study reveal that traditional PK-12 settings serve as the first spaces, the university teacher education programs as the second spaces, and the community-based settings as third spaces. Results showcase how these third spaces can be transformative for teacher educators as they consider preservice teachers’ learning. Findings are drawn from a 2-year collective self-study of four teacher educators facilitating community-based field experiences in the United States and Canada. Analysis of the teacher educator reflections of their observations revealed the transformative ways in which these community-based places, as separate and unique constructs, acted as a teacher for preservice teachers when working in community-based settings. This study presents arguments for integrating community-based field experiences within teacher education, particularly as such places can support and facilitate preservice teachers’ learning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".