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Record W4387665276 · doi:10.15173/ijsap.v7i2.5142

Student-staff partnership in India: A future possibility within contested terrain?

2023· article· en· W4387665276 on OpenAlexvenueno aff
Preeti Vayada

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipContext (archaeology)Thematic analysisReflexivityPublic relationsSociologyFocus groupPedagogyAdaptation (eye)Political scienceQualitative researchSocial sciencePsychologyGeographyAnthropology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.566
Teacher spread0.478 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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