Synthesis & Synergy: Finding Connection Across Spinocerebellar Ataxia Type 1, Knowledge Translation, and Higher Education Research
Bibliographic record
Abstract
Graduate students are socialized into three key domains of academia throughout their studies – research, service, and teaching. The outcome of this socialization is impacted by a student’s disciplinary affiliation, training environment, and supervisory relationship. This multi-disciplinary dissertation represents a scholarly examination of three examples of disciplinary research, service, and teaching. For disciplinary research, we explore characterizing the DNA damage response of ataxin-1, the disease-causing protein of the neurodegenerative triplet-repeat disorder Spinocerebellar Ataxia type 1. For service, we examine the positive impact of a knowledge translation platform for ataxia research ataxia patient and family member readers, as well as its volunteer writers and editors. For teaching, we investigate how the COVID-19 pandemic has impacted graduate students' and postdoctoral fellows' development due to laboratory closures. Further, we return to this examination of the influence of COVID-19 on academia through the exploration of disparities in publication pressure reported by scholars in Canada. Though seemingly disparate research topics, each line of inquiry is grounded within research pragmatism, namely the identification of practical solutions through a clear understanding of a phenomenon. This breadth of research would not be possible without interdisciplinary graduate training, which develops scholars adept at creating innovative solutions to complex or ill-defined problems. Overall, this dissertation offers a snapshot of the opportunities and challenges of interdisciplinary research training within a biomedical research department.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".