Navigating the PhD journey: a collective consideration of junior academics in qualitative accounting and management research
Bibliographic record
Abstract
Purpose The purpose of this paper revolves around the informal coming together of various doctoral students in the area of qualitative accounting and management research and the attempt to learn from their respective experiences. Together, the authors share personal reflections and valuable insights in revealing their vulnerabilities, aspirations and how they make sense of the PhD journey and their becoming as academics. Design/methodology/approach This paper builds on an open discussion and written reflections among the authors, who represent a diverse set of both doctoral students at various levels and recent graduates from different countries, schools and backgrounds. Findings The discussion highlights the struggles the authors experience as doctoral students, how they learn to cope with them as well as how they are socialized throughout their PhD journey. This allows them to take a critical stance towards increased productivity demands in academia and to embrace doctoral students as a powerful collective, whose aspirations may inspire a change of academic reality for the better. Originality/value While guidance on how to succeed as doctoral students is common, we seldom hear about doctoral students as particularly “fragile selves” (Knights and Clarke, 2014) who, as opposed to more established scholars, are more actively experiencing difficulties with finding their ways in academia. The authors are thus motivated to create a rare common voice of a group of doctoral students here by providing a more intimate account of the PhD journey.
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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 | 0.185 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.032 | 0.047 |
| Scholarly communication | 0.031 | 0.016 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".