ENHANCING CO-OP AND CAREER DEVELOPMENT ACTIVITIES THROUGH A STUDENT-DRIVEN MENTORSHIP PROGRAM
Bibliographic record
Abstract
Abstract – McMaster’s University Bachelor of Technology (B.Tech.) Program has a mandatory 12-month cooperative (co-op) work experience as part of its academic requirements for graduation. To assist students in attaining co-op opportunities they must enroll in a career development credit course to equip them with vital knowledge and tools necessary to obtain and retain co-op work experiences. Students also receive ongoing support and guidance from Engineering Co-op & Career Services (ECCS) department, which connects students with employers and provides individual career counselling services. Despite this training and the availability of services, many students struggle to obtain workplace co-ops. In response the School of Engineering Practice and Technology (SEPT) implemented an undergraduate career peer co-op mentoring program as a further support mechanism to engage and motivate students. A pilot mentorship program was launched in 2014-15 for a select group of students and based on the positive response; an ongoing program was adopted and has run for the last two years. The program is formal in nature with a senior student mentor randomly matched with approximately 10-12 junior students as their mentees. To date, the program has impacted 362 second-year students (the mentees) and 36 senior students (the mentors). For the purposes of knowledge sharing, the paper will discuss the benefits of peer mentoring, the design and structure of the SEPT undergraduate career co-op peer mentoring program, feedback from participants, along with lessons learned from the outcomes of the last three years.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".