The New Normal? The Practice of Doctoral Education in a Global Pandemic
Bibliographic record
Abstract
There has been an increasing interest in doctoral education in recent years, and an increase in the number of doctoral graduates as institutions and nations strive to be competitive in the global knowledge economy, making doctoral education a prescient issue. There is very little existing literature on doctoral education in Canada. In the broader, existing literature, a socialization framework is frequently employed to explore the student’s journey from novice to scholar. A significant factor in this socialization has been the global COVID-19 pandemic, with the Canadian response first being felt in March 2020. In some provinces, some of the COVID-19 social distancing measures remained until early 2022. To address the broad aim of this research — to attempt to understand doctoral students’ perceptions, experiences, and socialization during the COVID-19 pandemic, contextualizing these aspects using Bourdieu’s conceptual tools — the following two-part research question was addressed: Firstly, how were students’ perceptions and experiences of their doctoral programs affected in the immediate response to the COVID-19 pandemic? And secondly, how can Bourdieu’s conceptual tools help us conceptualize students’ experiences? Semi-structured interviews were conducted with 18 doctoral students across various disciplines and departments at three research-intensive universities in one Canadian province. A fundamental aspect of this thesis was the application of Pierre Bourdieu’s conceptual tools of habitus, capital, and field, both in the design of interview questions and examination of the participant responses, to explore doctoral student experience both generally and with specific reference to experiences between March 2020 and the date of their interview, with the final interview occurring July 2021. The results suggested that previously described factors affecting socialization were exacerbated during this time. The expectations placed on students and the valued activities for socialization remained unchanged, but how students perceived those expectations and experienced those activities changed significantly. The research revealed that long-standing assumptions of doctoral study as leading to an academic faculty position are not only outdated but may be actively hindering socialization. This research advances that a new field of doctoral education has emerged due to pandemic adaptations, which has implications for the design of future doctoral programs.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.046 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.074 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".