ABSTRACT CANADIAN LIS EDUCATION: TRENDS AND ISSUES
Bibliographic record
Abstract
Interviews with the directors or program chairs of Canada’s seven graduate schools of library and information studies revealed that schools face four common issues: (1) relocation within the university administrative structure or partnership with other academic units, (2) introduction of broader curriculum menus and more flexible course schedules, (3) closer liaison with industry for research funding advantage, and (4) introduction of courses and assignments which encourage entrepreneurial attitudes and abilities. Trends which emerged include the participation of some schools in “non-library ” undergraduate programs, the increasing number of joint degree programs (an MLIS combined with another master’s degree), the slow but steady emergence of distance education courses, and a concern with students ’ lack of enthusiasm for management courses. All but one Canadian school has at least one course that focuses on competitive intelligence strategies and/or entrepreneurial skills, which include long-range planning, risk-taking, applying business practices, and marketing.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".