Advancing Leadership and Team Research Through Second Uses of Meta-Analytic Data
Bibliographic record
Abstract
The symposium includes four studies that advance leadership and team research by adopting secondary uses of meta-analytical data, including systematic reviews of meta-analyses, second-order meta-analysis, and identification of original studies using meta-analyses. Supported by a cloud-based meta-analysis platform, these studies have the potential to summarize and integrate diverse research topics in large fields such as leadership and team. A systematic review of team meta-analyses Author: Yanzhe Zhou; School of Labor and Human Resources, Renmin U. of China Author: Jingxian Yao; School of Economics & Management, Tongji U. Author: Ming Lou; School of Management, Harbin Institute of Technology Building a large primary-study pool from team meta-analyses Author: Ming Lou; School of Management, Harbin Institute of Technology Author: Yanzhe Zhou; School of Labor and Human Resources, Renmin U. of China Author: Yitong Li; Furen International School, Singapore Author: Zhaoli Song; National U. of Singapore Assembling pieces into a whole: A systematic literature review of leadership meta-analyses Author: Tao Su; Guangdong U. of Technology Author: Jinlong Zhu; Renmin U. of China Author: Piers Steel; U. of Calgary A second-order meta-analysis on leadership effectiveness Author: Xiaoyu Li; Renmin U. of China Author: Boyuan Ju; National U. of Singapore Author: Bo Li; Business School of Liaoning U. Author: Zhaoli Song; National U. of Singapore Author: Piers Steel; U. of Calgary Discussant Author: Ernest O'Boyle; Indiana U.
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 | 0.482 | 0.679 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.037 |
| Bibliometrics | 0.040 | 0.031 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".