Re-envisioning Management Education and Training for Information Professionals
Bibliographic record
Abstract
The evolving demand for workforce skills has often been a topic of discussion at various professional library and information science (LIS) conferences and in the academic literature. Although LIS schools tend to highlight the goal of preparing future members of the LIS profession to be effective leaders, a management and leadership curriculum gap still exists worldwide. Most LIS schools are still falling short when it comes to offering advanced management courses on a regular basis as identified in international studies. Consequently, this lack of adequate management education and training in LIS schools tends to contribute to the development of “accidental managers.” A review of the online program and course descriptions of the 58 American Library Association (ALA) accredited graduate schools demonstrates the current landscape of LIS education in relation to the demand for leadership and management skills and the increasing significance of these competencies for information professionals. This research also shows how regular interaction and engagement with the LIS professional community resulted in the development of an online advanced certificate program in management for information professionals. Although this study primarily reviewed LIS schools in the U.S. and Canada, it has wider implications given the need for advanced management courses expressed in the literature worldwide. It is vitally important for LIS schools to bridge the existing gaps in LIS education in order to meet professional demands for leadership and management skills, and this paper demonstrates one way in which LIS schools can accomplish this.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".