Special Issue on New Paradigms in Evaluating Leadership Development
Bibliographic record
Abstract
The Problem Evaluation of leadership development programs has been criticized recently for being based too much on structured evaluation models that do not account for more broad based outcomes and interpretations of leadership development. The Solution This special issue develops some core themes around enabling and enacting leadership development evaluation in organizations. The focus is on highlighting innovative ways of evaluating leadership development that fits more comfortably with contemporary leadership theory. This link with contemporary leadership literature, in turn, brings about thinking around mindsets, culture, partnerships, relationships, and the interdependency of leaders and leadership in organizations, some of the core themes represented in this special issue. These themes are expressed as new paradigms as there appears limited discussion in the literature regarding their use in evaluating leadership development. The purpose of the special issue will be to highlight differing and innovative approaches to leadership development evaluation rooted in experiences of delivering such programs in the United Kingdom, Canada, New Zealand, and Australia. Each article expresses novel approaches to the evaluation of leadership programs that can be inculcated into practice across a wide variety of organizations. The Stakeholders Leadership development programs have started to become prolific in organizations both public and private sector based. This has provided a challenge to human resources professionals in the evaluation of such programs that are increasingly becoming complex in nature and starting to use innovative and unusual approaches. This special issue therefore delves into contemporary leadership theory and relates this to the practice of delivering and evaluating programs across organizations. The outcome therefore will help HR professionals identify key aspects to take account of when evaluating leadership development in and across organizations, and will set the basis for further empirical research and theoretical reflection on the topic.
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 | 0.028 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".