Doctoral Students’ Experiences of Feeling (or not) Like an Academic
Bibliographic record
Abstract
Aim/Purpose: This paper examined the balance and meaning of two types of experiences in the day-to-day activity of doctoral students that draw them into academia and that move them away from academia: ‘feeling like an academic and belonging to an academic community;’ and ‘not feeling like an academic and feeling excluded from an academic community.’ Background: As students navigate doctoral work, they are learning what is entailed in being an academic by engaging with their peers and more experienced academics within their community. They are also personally and directly experiencing the rewards as well as the challenges related to doing academic work. Methodology : This study used a qualitative methodology; and daily activity logs as a data collection method. The data was collected from 57 PhD students in the social sciences and STEM (Science, Technology, Engineering, and Mathematics) fields at two universities in the UK and two in Canada. Contribution: The current study moves beyond the earlier studies by elaborating on how academic activities contribute/hinder doctoral students’ sense of being an academic. Findings: The participants of the study generally focused on disciplinary/scholarly rather than institutional/service aspects of academic work, aside from teaching, and regarded a wide range of activities as having more positive than negative meanings. The findings related to both extrinsic and intrinsic factors that play important roles in students’ experiences of feeling (or not) like academics are elaborated in the study. Recommendations for Practitioners: Supervisors should encourage their students to develop their own support networks and to participate in a wide range of academic activities as much as possible. Supervisors should encourage students to self-assess and to state the activities they feel they need to develop proficiency in. Future Research: More research is needed to examine the role of teaching in doctoral students’ lives and to examine the cross cultural and cross disciplinary differences in doctoral students’ experiences.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".