Alone in the Academic Ultraperiphery: Online Doctoral Candidates’ Quest to Belong, Thrive, and Succeed
Bibliographic record
Abstract
Despite the increasing number of non-traditional doctoral researchers, this population’s experiences remain largely understudied and their voices unheard. Through in-depth interviews with 24 part-time online doctoral candidates, we explored the perceived facilitators and barriers to academic integration and sense of belonging, as well as how online delivery influences the doctoral journey. Reflexive thematic analysis revealed a strong drive for participation, sometimes matched by the supervisor but rarely supported by the institution, which in the end does not sufficiently promote community building. Online delivery was viewed as both a blessing for the accessibility it enabled and a curse due to pervasive feelings of isolation and virtually non-existent peer networks. Online doctoral researchers coped by breaking free from the fully online model whenever possible to seek in-person and synchronous interactions and guidance. We conclude that online doctoral candidates constitute an ultraperipheral population in the academic landscape. Support provided by online PhD programmes should be modelled after the actual needs of their non-traditional students.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".