Evolution of a summer research/writing workshop for first‐year university students
Bibliographic record
Abstract
Purpose The paper aims to describe a framework for a summer research/writing workshop for new university students, and its evolution over time and across institutions. Design/methodology/approach The University of Toronto at Scarborough (UTSC) has successfully offered its award‐winning two‐day Summer Learning Institute on Research, Writing and Presentation Skills for four years (2004‐2007), to increasing enrolments. Memorial University of Newfoundland (Memorial) adapted the UTSC model and successfully piloted its four‐day workshop, Summer Program in Academic Research and Communication (SPARC), in August 2006. Both programs were low‐cost, non‐credit summer workshops for new students to help them prepare for university‐level research and writing. Memorial offered its program a second time in August 2007. This paper focuses on the Memorial experience. Findings The success of these programs is attributed to a common framework used in each case: program planning, marketing, and delivery and assessment. Practical implications The framework described in this paper could be adapted by other institutions wishing to implement such a program. In addition, the SPARC team will continually improve the program by reflecting on each part of the framework. Originality/value Much research has been done to identify and address the specific needs of first‐year university students. For example, some institutions offer “first year experience” courses for credit, while others place first‐year students into Interest Groups. Most academic libraries offer information literacy sessions to groups of undergraduate students during the regular semester. This paper presents a model for reaching first year studentsbeforethey take their first class at university.
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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 | 0.044 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".