Supervising transdisciplinary doctoral research: adopting transcendent and transgressive supervisory strategies
Bibliographic record
Abstract
It was ten years ago that my colleagues and I fi rst wrote about ‘interdisciplinary’ doctoral education and supervision pedagogies (Manathunga, Lant and Mellick 2006). Although, as Klein (1996) emphasizes, ideas about interdisciplinarity or transdisciplinarity have been around since at least the 1930s, transdisciplinary doctoral education was still a relatively new phenomenon at the beginning of the twenty-fi rst century. To emphasize this, we called our piece ‘Imagining an interdisciplinary pedagogy’. We reviewed a number of doctoral programs around the globe that claimed to be interdisciplinary and developed a number of ‘key dimensions of a potential interdisciplinary doctoral pedagogy’ including: • relational, mediated, transformative and situated learning experiences; • intercultural knowledge and skill development; • enhancing candidates’ higher order thinking and metacognitive skills; • enhancing candidates’ epistemological understandings of their original discipline. (Manathunga et al. 2006, 368) We also suggested that there was a need to follow four principles: • create spaces for interdisciplinary dialogue – relational; • engage in interdisciplinary interaction with others and with the texts and tools of a number of disciplines – mediated; • synthesize disciplinary knowledges in order to produce original, creative ideas and futures – transformative; • create personal, interpersonal and communal intellectual contexts conducive to interdisciplinary exchange – situated. (Manathunga et al. 2006, 369) If anything, the imperatives for transdisciplinary research and doctoral education have become even stronger in the last 10 years. This was especially brought home to me in my recent role as an external reviewer of an effective transdisciplinary Doctor of Social Science program in Canada. Increasingly, doctoral candidates conduct research that moves across and between the university, industry, business, community and other sectors. The need for both supervisors and candidates to have excellent intercultural communication skills and knowledge in order to navigate the multitude of sub-cultures, discourses and languages associated with transdisciplinary and cross-sectoral research (Manathunga 2009) has become even greater. All of these trends require supervisors who have high levels of epistemological fl exibility so that they may help their doctoral candidates to engage sensitively and equitably with diverse disciplinary, sectoral and cultural knowledge. These trends also mean that supervisors need effective teamwork skills in order to manage increasingly complex transdisciplinary supervision teams. Therefore, it is highly likely that recent insights into effective transcultural supervision pedagogies (Manathunga 2014) may also have resonance for the development of a refl exive approach to transdisciplinary doctoral education and supervision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".