Internationalization and the Canadian Academic Library: What Are We Offering?
Bibliographic record
Abstract
Although the population of St. John’s, Newfoundland, is largely homogenous recent Census data reveal that its inhabitants are 98% Englishspeaking and visible minorities make up around 1% of the population one can’t help but notice that from behind the Information and Research Help desk at the Queen Elizabeth II Library (QEII) of Memorial University of Newfoundland (MUN), things look a lot more diverse than reflected in the general population (Statistics Canada, 2006, 2012). Despite the remoteness of the province and its small size (around half a million people call it home), what is happening on the MUN campus is part of a greater trend in academia affecting university campuses all over Canada and the United States, which is the rise of the international student. This research study was undertaken to examine the role of the library within the Canadian academic institutions these international students attend, and the services that are being offered to them. The main questions we sought to answer are: do academic libraries in Canada offer specialized services for international students? And, if so, what types of programs are being offered? This information will be useful for future program development at our institution, and other universities and colleges throughout Canada and the rest of the world.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.047 | 0.017 |
| Scholarly communication | 0.037 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.023 | 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".