Student-staff partnership in India: A future possibility within contested terrain?
Bibliographic record
Abstract
Existing literature on named “students-as-partners” (SaP) practices suggest that these practices are still in their embryonic stage in Southeast Asia. In addition, the literature on SaP that supports it as a global practice has not examined the suitability of student partnership (SP) practices in Indian higher education (HE) yet. It is here that this paper seeks to contribute by examining Indian students with experiences in being a student partner in an Australian university to reflect on the challenges and possibilities in relation to implementing these practices in Indian HE. Adopting a postcolonial theoretical frame, this paper examines empirical evidence from an informal group discussion among six Indian collaborators. The data, analysed using a reflexive thematic analysis suggested by Braun and Clarke (2019), provide four interrelated themes that focus on the suitability of these practices in India intertwined with the inherent challenges. The study argues in favour of context-specific adaptation of the practice. Through the Indian students’ experiences, it adds to the ongoing conversations about knowing and understanding SaP in new ways and about positioning students as knowledgeable individuals, which is at the core of partnership practices. By bringing the voices of Indian students to the fore, this article argues in favour of embodying SaP as culturally relevant or a decolonising 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".