Guiding Questions: Guidelines for Leadership Education Programs
Bibliographic record
Abstract
Appropriate design and redesign of programs, responses to accreditation agencies, and internal academic legitimacy concerns are critical challenges in education in general and in leadership education in particular. Guiding Questions: Guidelines for Leadership Education Programs (Guiding Questions), a member initiated project sponsored by the International Leadership Association (ILA), provides a framework to address these challenges. In this paper, we first describe the background and context of this initiative. Second, we introduce the Overview of the five sections of Guiding Questions: Conceptual Framework, Context, Content, Teaching and Learning, and Outcomes and Assessment. Third, we present results of initial field tests of the Overview and its framework within three different North American universities. Finally, we discuss next steps and invite the reader to get involved in the further development of Guiding Questions.
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 | 0.082 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.010 |
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".