Using review articles to address societal grand challenges
Bibliographic record
Abstract
Abstract We introduce a special issue of International Journal of Management Reviews that demonstrates how to use review articles to address societal grand challenges—complex, large‐scale issues facing humankind, such as climate change, inequality and poverty. First, we argue that review articles possess unique features that make them particularly useful for addressing societal grand challenges. Second, we discuss three distinct but related roles of review articles in addressing societal grand challenges: (1) advancing theoretical knowledge; (2) advancing methodological knowledge; and (3) advancing practical knowledge. We conclude by providing future directions to enhance contributions of review articles for addressing societal grand challenges further by: (a) spanning disciplinary boundaries; (b) engaging practitioners; and (c) using alternative review approaches.
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 | 0.104 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".