Professional-personal-private boundaries in a Supervisor - PhD student relationship - The influence of cultural backgrounds
Bibliographic record
Abstract
In any job that requires contact with people, there is a need to find a “healthy” balance between professional and personal life in order to function at work. In a way, the PhD student-supervisor case is not different from such jobs. As stated in a recently published article, “Because of the delicacy and complex power dynamics, supervisor- student relationships need clearly defined boundaries.” The requirements and understanding of where that balanced line or boundary lays might depend on the individual person, but those differences seem to be milder within a particular culture. In this sense, the PhD student-supervisor situation might be an extreme case due to the often multicultural environment of an international university. There is very little information in the Internet about specific boundaries between professional-personal-private life in a PhD student-supervisor context. Some information about how these boundaries differ between cultural backgrounds or nationalities can be found in the “Guide to mentoring graduate students across cultures” developed by the Western Teaching Support Centre, University of Western Ontario, Canada. 2 This handbook highlights general differences in personal- professional boundaries among different nationalities. For example, while in North America people distinguish between work friends and personal friends and socialize separately, students from Central America and Mexico expect supervisors to take care of them as part of an “extended family”. A parental supervision style is also expected among Chinese students, who find it appropriate to ask for a loan or to borrow the supervisor´s car. In return, these students show firm dedication to the supervisor´s work. In our work, we decided to conduct interviews with the aim to explore how specific boundaries might change for people, with a special focus on the cultural background of the interviewees. For this purpose we have developed two parallel questionnaires, one directed to graduate students and the other one directed to supervisors, to try to figure out if there were any changes in such boundaries regarding nationalities, gender, age and years of experience. The questions have focused in six different criteria, named 1-personal relationships, 2-dual relationships, 3-political view, 4-personal favors, 5- financial situation, 6-health. For each criterion, an open question and a yes/no question were asked. The latter was done to be able to quantify some of the answers. We have interviewed 11 supervisors (9 Swedes, 2 Non Swedes) and 14 graduate students (8 Swedes, 6 Non Swedes).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".