Practice schools as third spaces? Navigating the continuum between hierarchical models and collaborative partnerships in teaching practice in South Africa
Bibliographic record
Abstract
Mentoring student teachers is a fundamental approach in teaching practice. Traditionally, teaching-practice models have been based on cognitive apprentice approaches and have been hierarchical in nature. Problems with finding suitable schools for teaching practice and mentoring experiences have been challenging, which has led to the implementation of collaborative partnership approaches. In South Africa, minimal research has been undertaken on the establishment of partnerships to strengthen teaching practice, with work in the third space almost non-existent. The research question under investigation in this paper is as follows: How do we move away from hierarchical models of teaching practice and establish collaborative partnerships between schools and universities? The paper is underpinned by both third space theory and border theory. The aim of the paper is, first, to explore the challenges encountered with the hierarchical models used in teaching practice. Second, we explore what collaborative educational partnerships entail and investigate the various models used internationally to establish partnerships between universities and schools to strengthen teaching practice. This non-empirical paper uses a secondary-source data design that draws on existing texts, research findings, and journal articles. A qualitative research approach has been employed as it allows a narrative description of the data collected. An interpretive approach is employed to interpret and to discuss the findings. The paper concludes by reviewing practice schools as a type of school that allows lecturers, teachers, and students to cross institutional borders and collaborate in the third space.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".