Weaving Together: An Exploration of Succession Planning in Rural Canadian Post-Secondary Institutions
Bibliographic record
Abstract
There is a real concern about the increasing vacancies for corporate and higher education leadership positions. It is essential that planning and preparing take place for these vacancies. However, despite an ever-evolving body of organizational research literature regarding succession and leadership development, there is a perceived gap in the evidence base on best practices for higher education, particularly within Canada, and especially in rural Canadian post-secondary institutions. This primarily qualitative study uses an informed grounded theoretical framework with a social constructivist lens. By learning more about the succession planning experiences of those who are likely to be subjects and initiators of succession planning systems, this study aims to inform future developments in succession planning in Canadian post-secondary institutions operating in rural centres. Based on semi-structured interviews and questionnaires with post-secondary leaders working in seven of the 13 Canadian provinces and territories a new model was formed through an iterative and reflexive analysis of the data, emergent themes, and literature. The new model provides a framework which employees, supervisors, and institutions could use to support constructive dialogue and action plans for individuals to hone their leadership practice fit for Canadian post-secondary. Challenging the common pipeline metaphor, the model proposes weaving together learning-by-doing opportunities through multiple touchpoints with people throughout the organization and beyond. Additionally, the model encourages institutions to consider the desired leadership competencies and accountabilities to facilitate a cohesively-, yet organically-, built succession planning system. A system based on human connection and conversation with the goal to meet institutional leadership workforce needs, and optimize employees’ career goals. In this way, it is hoped the anticipated leadership deficit can be alleviated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".