Doctoral students’ identity positioning in networked learning environments
Bibliographic record
Abstract
In this study, the authors explored identity positioning as perceived by doctoral learners in online, networked-learning environments. The study examined two distance doctoral programs at a Canadian university. It was a qualitative study based on methodologies involving open coding and discourse analysis. The social positioning cycle, based on social positioning theory, was used as a theoretical lens guiding the analysis and organization of the data. The results indicate that distance doctoral students experience identity positioning across multiple aspects of their lives including, but not limited to, their social, intimate, professional, and academic contexts. The participants’ descriptions of their experiences highlight their ways of managing their relationships—through degrees of sharing, withdrawing, prioritizing, rationalizing, and changing practices. The results of this research suggest a need for additional studies of the distance doctoral experience and how institutions can better support learners and increase completion rates.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".