Socialization and Social Capital in Online Doctoral Programs
Bibliographic record
Abstract
Online doctoral programs are gaining in popularity, both among students and institutions. However, research to date on the effectiveness and popularity of such programs has looked largely at either measures of student satisfaction or of administrative effectiveness and design. Further, previous research has also tended to focus on the early part of doctoral study, particularly coursework. This mixed method study, conducted on three different programs within in a department of educational research in one university in UK will contribute to the literature in two important ways. First, it will look specifically on current and recently graduated student experiences from of the thesis component of the doctoral program using a demographic and experiential survey and following up with more in depth interviews to better understand students' motivation and goals for enrolling in their program and what kinds of academic experiences and knowledge they both bring to, and receive from, their program. Second, we will analyse the data through two lenses, that of academic socialization to help identify how academic identity changes over time, and that of social capital to help us understand the individual trajectories of students through their programs. Results will contribute both theoretically and practically to our understanding of student experience of the thesis process in online doctoral programs.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".