Strategic planning in higher education: lessons learned from the leaders at one Canadian Institution
Bibliographic record
Abstract
AbstractHigher education institutions around the world play a critical role in shaping our very way of life, building the intellectual capacity, knowledge utilization, scientific and technological skills that nations need to strive in today's highly competitive and interconnected global economy. Most of these institutions rely on strategic planning at their primary management tool to drive targeted change across their campuses in areas such as policy development, teaching and learning, student success, instructional resources, faculty professional development, and even facility improvements. And yet, many of them continue to report how it remains a major challenge to employ this important planning approach to its full potential. The purpose of this study was to conduct an in-depth qualitative examination of the views of the strategic planning leaders at one Canadian College for further insight into how these institutions could make better use of the process to help them survive, and even thrive in this context, to the benefit of not only their own constituents, but also the whole of society. This study analyzed the leaders' views in comparison to the existing strategic planning models, the prevalent challenges with strategic planning in higher education, and relevant theoretical frameworks on leadership to explore new insights on this issue. The study findings led to a series of practical, evidence-based lessons learned on where these institutions should focus their efforts to optimize their use of strategic planning. Key insights include that the institutions should start by ensuring that the entire community is on the same page in terms of not only its strategic vision and targeted priorities, but also its chosen strategic planning model. A structured, yet collaborative approach should be employed, with a focus on inclusive, equitable, transparent and accountable practices to engage everyone, especially the front-line faculty members, in the process. Furthermore, those who lead the strategic planning process should be highly committed to the strategic vision, well equipped to facilitate the process, and ensure that the process remains highly responsive to the volatile planning environments often encountered by these institutions.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".