The Four Steps to Exceptional Leadership of Campus Recreation in Turbulent Times
Bibliographic record
Abstract
Bob Dylan's “The Times They Are A-Changin”' describes the situation facing most college and university leaders as a result of the recent economic downturn. All financial commitments are under review, if not attack. Strong, executive leadership has always been needed in campus recreation, and the field has been well served by great leaders throughout time. That said, there are new challenges to address, due in large part to economic realities. Leadership theorists of the day call for a new type of leadership, one that is also well suited to the campus recreation area. This article tracks the latest developments in leadership and encourages directors to adopt (or reaffirm) four leadership practices that will help them lead their programs more effectively in addition to positioning the programs for ongoing support during these challenging economic times. As Bill George (2009, p. 2) noted in his recent book entitled Lessons for Leading in Crisis, a “smooth sea never produced a strong mariner.” Perfect storms are developing across our respective campuses.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".