Reasons, motives and motivations for completing a PhD: a typology of doctoral studies as a quest
Bibliographic record
Abstract
Purpose This study aims to examine how PhD students with diverse profiles, intentions and expectations manage to navigate their doctoral paths within the same academic context under similar institutional conditions. Drawing on Giddens’ theory of structuration, this study explores how their primary reasons, motives and motivations for engaging in doctoral studies influence what they perceive as facilitating or constraining to progress, their strategies to face the challenges they encounter and their expectations regarding supervision. Design/methodology/approach Using a qualitative design, the analysis was conducted on a data subset from an instrumental case study (Stake, 2013) about PhD students’ persistence and progression. The focus is placed on semi-structured interviews carried out with 36 PhD students from six faculties in humanities and social sciences fields at a large Canadian university. Findings The analysis reveals three distinct scenarios regarding how these PhD students navigate their doctoral paths: the quest for the self; the intellectual quest; and the professional quest. Depending on their quest type, the nature and intensity of PhD students’ concerns and challenges, as well as their strategies and the support they expected, differed. Originality/value This study contributes to the discussion about PhD students’ challenges and persistence by offering a unique portrait of how diverse students’ profiles, intentions and expectations can concretely shape a doctoral experience.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".