Using Virtual Cohorts for Wellness, Problem-Solving, and Leadership Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter explores the efficacy of virtual cohorts and how they may positively affect both leadership skills and wellness for emerging and current leaders. The authors initially met at Harvard University's Leadership Institute for Academic Librarians (LIAL) program in 2018 and then continued to meet virtually on a regular basis for the following four years. Cohort meetings emphasized practicing the skill sets taught at LIAL. This included both case study writing and Lee Bolman and Terrence Deal's “four frames” model. The authors self-administered surveys to assess the impact of participating in the cohort on a number of criteria including perceived value of the cohort, impact on the skill sets prioritized by the cohort, perceived wellness benefit during the trials of COVID-19, and cohort influence and/or impact on career progression. The chapter also includes recommendations for the development of future cohorts including best practices for scheduling, membership, and cohort focus.
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.
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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it