One step at a time: A narrative collaborative self-study exploring the challenges faced when conducting research as M.Ed. students
Bibliographic record
Abstract
The changing conditions and dynamic processes of conducting research can present various challenges for novice researchers. This study aimed to explore the narratives of four M.Ed. students’ research experiences. This presentation will narrate a reflective examination of each student’s varied experiences regarding the research design process and unique challenges encountered and effective strategies employed as novice researchers. This collaborative self-study included two phases: first, an independent reflection and analysis of research related documents and second, a collaborative thematic analysis to combine, organize and construct a multi-narrative of all four researchers. As collaborative storytellers, we were able to articulate a more dynamic narrative that included various research experiences entailing different methodologies, assorted recruitment strategies, and diverse collaboration and support systems. Through this investigation we aim not only to provide insights which will improve our own research practice, but also to aid future emerging researchers, researching mentors and educational programs to better support novice researchers.
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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 | 0.036 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".