Preparing the Leaders of Tomorrow: A Model of Applied Research Training in a Community College
Bibliographic record
Abstract
Depending on their program of study, many students graduating from colleges and universities will have had few educational opportunities to learn about the influence of a changing Canadian demographic. However, the reality is that an aging population can be expected to impact their careers regardless of their chosen field of work. At Sheridan College, the Sheridan Elder Research Centre (SERC) has developed a comprehensive applied research training and mentorship program for college students. This training is designed as a week-long immersive and interactive series of workshops. In addition to laying a solid theoretical foundation, exploring current issues in the field of aging and the principles of applied research, students participate in role-playing and team-building exercises that build a variety of applied research and communication skills. The use of a newly designed applied research board game is one of the innovative ways that SERC helps to build interdisciplinary relationships while teaching applied research skills. Two years after implementation, SERC has seen the effects of the program on student success within the classroom and after graduation. The training materials support SERC’s research goals on a variety of age-related topics while showing students how to apply the skills they learn in the classroom to ‘real world’ problems. This model for training the leaders of tomorrow is flexible, relatively simple to implement and has lasting benefits for both students and researchers. The curriculum and program outcomes will be discussed along with a demonstration of how the board game is used as a teaching tool.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".