Online Information Session: an Alternative Channel for Information Flow between Canadian Universities and Prospective Students
Bibliographic record
Abstract
As a Canadian citizen of Chinese origin, I am aware of the language and cultural barriers high school students from China face when attempting to gather information online for selecting a school in North America for higher studies. Systematic research revealed a gap between the information channels they resort to and the channels through which universities approach prospective students. I attempted to bridge the gap by exploring effective alternative information channels. I examined the needs of prospective Chinese students through preliminary research with current students at OCAD University. I also explored Baidu Forum and WeChat, two online social media channels most used by Chinese students. I designed an online information session where universities could provide information to prospective students in China in real time, and conducted two sessions, one from Toronto, Canada and one from Tai Yuan, China. My \nrecommendations to OCAD University about effective ways to reach prospective students from China could be extrapolated to other universities and countries.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".