Conducting a PhD as a project: sharing insights from my doctoral journey
Bibliographic record
Abstract
Purpose The aims of this paper are to mobilize project management concepts in order to draw parallels with the doctoral project and to share the author’s experience and lessons learned. Design/methodology/approach This paper is based on a qualitative inquiry using an autoethnographic approach. Findings This paper contributes a self-reflexive examination of the doctoral project experience that incites current and future doctoral students and early-career researchers to take advantage of opportunities that make the experience satisfying, lay the foundations of an academic career and help ensure the thesis is completed in a timely and orderly manner. Originality/value Examining the doctoral project through the lens of one of the highest standards in project management, developed by the Project Management Institute (PMI), this paper enables PhD students in project management and other fields of study to understand the basics of a project and take action to structure their doctoral journey in a way that enhances both their experience and chances of success.
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 | 0.033 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".