Leveraging Resources Across Units and Universities to Address Academic Literacies and Research Skills in Ontario Graduate Students
Bibliographic record
Abstract
Student2Scholar (S2S) is a fully online and open course that aims to teach academic literacies and research skills to social science graduate students. Set to launch in December 2015, S2S was conceived of and created by a diverse and distributed team of academic librarians, university staff, and graduate students from three Ontario Universities: Western, the University of Toronto, and Queen’s. Members of the project team brought with them varying degrees of experience and expertise across a range of disciplinary and teaching and learning backgrounds, including: adult education, information literacy, and online learning (to name only a few). S2S serves as a standout example of what can be achieved when a teaching and learning project is resourced to leverage the time and talent of a cross-section of the academic community whose professional goals and educational interests are shared, despite working in seemingly disparate and often disconnected areas of campus or institutions of higher education. This poster presentation will highlight the pedagogical (i.e., conceptual and theoretical) framework used in the design S2S, and make explicit the connections between the design of the course and the human resources required, and ultimately assigned to contribute to the development of the course (e.g., organizational development, design and development of modules and assets, writers, etc). Using S2S as a case study in online, module-based, interdisciplinary course development, MIIETL conference delegates will learn how to leverage established and yet-to-be formed relationships across academic units and institutions to realize mutually beneficial teaching and learning outcomes.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".