Just Plain Rhetoric? An Analysis of Mission Statements of Canadian Universities Identifying Their Verbal Commitments to Facilitating and Promoting Lifelong Learning
Bibliographic record
Abstract
Over the past decade, many government and policy documents have highlighted with greater urgency the need for lifelong learning. What do present mission statements of Canadian universities have to say regarding lifelong learning? In researching this question we analyzed the institutional mission statements of 58 Canadian universities to identify the extent to which these expressed commitments to facilitating and promoting lifelong learning. While mission statements cannot serve as proof of institutions actually enacting the goals and ideals by which they choose to portray themselves to the public, they still yield insight into the values institutions recognize as important. This article first proposes a model of conceptualizing lifelong learning in higher education. It distinguishes three dimensions of lifelong learning (the adaptive, the personal, and the democratic) and two aspects (lifelong learning as a goal and life-long learning as a process). From this three by two matrix we derive six categories of lifelong learning in or through higher education. We then use these six categories as a priori codes for our deductive anal- ysis of mission statements. We present and discuss the outcomes of our study, note differences with regards to institutional type, and make some suggestions for future research.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".