Bibliographic record
Abstract
Fall is around the corner and soon we will have the opportunity to meet at the annual International Leadership Association (ILA) meeting in Vancouver. The Journal of Leadership Studies' (JLS) roundtable discussion at ILA is scheduled for the afternoon of Friday, October 13. Set aside your paraskevidekatriaphobia (fear of Friday the 13th) and please join us to learn more about the journal and how you can get involved as an author, reviewer, or editor. Speaking of editors, I am sad to announce that our Qualitative Methods Editor for the past few years, Dr. Corey Seemiller, is stepping down from her editorial position. During her time at the journal, Dr. Seemiller provided exceptional leadership and guidance for the qualitative section of the journal. As many of you know, Dr. Seemiller is both an expert on leadership as well as Generation Z. Thank you Dr. Seemiller for your work with the journal and good luck with the many activities you have planned. Continuing the editorial theme, I will soon be sending out a call for editors. In addition to replacing Dr. Seemiller as Qualitative Methods Editor, we will be searching for a Quantitative Methods Editor(s), as well as multiple Associate Editors. If you are thinking about the characteristics of a good editor, let me share that I have found the best editors have reviewer experience with the journal and are committed to working collegially with authors. Watch your email for the call later this fall. Now, on to the articles in this issue of the journal. It is really one of the most interesting issues I have been involved with, especially the symposium. Guest Symposium Editor Dr. Caitlin Bletscher has assembled leadership scholars from around the world to highlight the worldwide issues related to refugees and the role leadership can, and does play in assisting refugees. The seven-article symposium highlights the many facets and issues related to refugees and the challenges they face across the globe. Not only did I appreciate learning more about those challenges, but I was also delighted to read about the work leadership scholars, lawyers, and practitioners are engaged in to address the refugees' challenges. The current issue opens with Purcell and Smith's article (Disciplinary Expertise and Faculty Credentialing in Leadership Studies: Advancing a Necessary Conversation) exploring the field of leadership and how, given the diversity in the field, leadership studies needs to have the conversation about the credentials and background of those engaged in leadership activities. The authors do a great job of addressing the many issues regarding credentialing. In closing, I am going to tease our next issue and note that we will likely publish our first Editor's Thoughts articles. I hope you have the chance to read the current issue before your summer is over.
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".