Solving Societal Grand Challenges: A Debate and Future Directions
Bibliographic record
Abstract
Despite increasing attention of management scholars to the study of societal grand challenges and the progress made with promoting sustainability programs in the private sector, societal challenges such as economic inequality, public health hazards, climate change, and social divide, have worsened. The purpose of this symposium is to bring together leading scholars to engage in a debate and discuss their views and research concerning established and emerging approaches in management research for solving societal grand challenges. The panelists will identify advantages, limitations and contingencies of focused change interventions and broad system changes as well as debate the private versus public responsibilities for solving societal grand challenges, while considering new forms of economic systems and organizational governance. Thus, the panelists will share diverse views on the topic and deliberate with the audience about emerging perspectives, practices, and promising avenues for future research on solutions to societal challenges.
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 | 0.046 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.023 | 0.038 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.036 | 0.024 |
| Insufficient payload (model declined to judge) | 0.016 | 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".