Bibliographic record
Abstract
What are the benefits or drawbacks of a low-residency educational delivery model? How does the process of designing one's own study impact the work completed in a distance education program with this form of delivery? As researchers who found success in a low-residency undergraduate program, we engaged in a duoethnographic study to mine our experiences and better understand the advantages and disadvantages of this educational model. We engaged in four recorded conversations over the course of three weeks, with sessions ranging from 35 to 60 minutes each. Between sessions, we journaled in a shared online document, discussing our emerging understandings of the topic, responding to each other's perspectives, and pushing one another to articulate and revisit our stances. Through these oral and written dialogues, we identified six themes featured in the low-residency educational model: reduced stigma for non-traditional students, diversity of community, flexibility, self-designed study, staying connected, and clarity of boundaries. Keywords: low-residency, distance education, duoethnography, self-designed learning, non-traditional students
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".