Presentation on Peer Mentorship
Bibliographic record
Abstract
According to Statistics Canada, over 2.1 million students enrolled in Canadian public universities and colleges for the 2017/2018 academic year (Stat Can, 2020). From a global perspective, this number is astronomical. Reports indicate that during this same time period, Canada was the most educated country in the world, with over 56-percent of adults aged 25-64 having been educated at the post-secondary level (CNBC, 2018). This, of course, is a great achievement for Canada, however one unfortunate biproduct of having such a large population of enrolled students is that the number of students who do not reach graduation is also relatively high. In 2018, Maclean’s ranked the top 49 universities in Canada by degree completion rates (Maclean’s, 2018). The magazine found that only six out of the 49 universities studied had degree completion rates of 80-percent or greater. Worse yet, the average completion rate for all 49 universities listed was only 71.3-percent. That remaining 28.7-percent represents hundreds of thousands of students annually who will experience the financial and psychosocial repercussions associated with ‘dropping out’. This is not only disadvantageous for these individuals, but for Canada’s workforce as well, due to the loss of many specialized workers. Peer mentorship programs have been presented as a cost-effective solution to this problem, however more research is required in terms of design, implementation, and evaluation of outcomes. Our study will seek to help close these gaps.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.408 | 0.177 |
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".