Can (Post-Heroic) Leadership Be Taught (Online)? A Library Educator’s Expansion of Baldwin, Ching, and Friesen’s Grounded Theory Model of Online Course Design and Development
Bibliographic record
Abstract
Many Master of Library and Information Science (MLIS) programs are now offered online, and most of these programs offer courses on leadership and management principles. Teaching leadership in any context presents challenges because leadership is a hazy and confounding concept. The intrinsic problems in teaching leadership are compounded by the professional context of libraries; librarianship is a feminized profession whereas being a leader is often a male-oriented construct. This confounding mix of teaching leadership informed by feminist theory is magnified by the challenge of teaching online, where the harassment of women academics (such as MIT’s Chris Bourg) is pervasive and destructive. There is a paucity of research and discussion on how to design online leadership courses in graduate MLIS programs that account for these challenges. This paper contributes to this discussion by expanding upon Baldwin, Ching, and Friesen’s grounded theory model of online course design and development. Grounded theory is an experiential methodology, and this paper aligns with Baldwin et al.’s grounded theory approach by applying constant comparison between the author’s experience designing an online graduate-level leadership course and their model.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".