Generating Benefits and Negotiating Tensions through an International Doctoral Forum: A Sociological Analysis
Bibliographic record
Abstract
Workshops and seminars are widely-used forms of doctoral training. However, research with a particular focus on these forms of doctoral training is sporadic in the literature. There is little, if any, such research concerning the international context and participants’ own voices. Mindful of these lacunae in the literature, we write the current paper as a group of participants in one of a series of doctoral forums co-organised annually by Beijing Normal University, China and Queensland University of Technology, Australia. The paper voices our own experiences of participation in the doctoral forum. Data were drawn from reflections, journals, and group discussions of all 12 student and academic participants. These qualitative data were organised and analysed through Bourdieu’s notions of capital and field. Findings indicate that the doctoral forum created enabling and challenging social fields where participants accrued and exchanged various forms of capital and negotiated transient and complex power relations. In this respect, the sociological framework used provides a distinctive theoretical tool to conceptualise and analyse the benefits and tensions of participation in the doctoral forum. Knowledge built and lessons learned through our paper will provide implications and recommendations for future planning of, and participation in, the doctoral forum series and similar activities elsewhere.
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 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 | 0.053 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".