What Now? What Are We Missing? What Do We Need to Learn and Change as Management Scholars?
Bibliographic record
Abstract
This proposed panel symposium explores how management paradigms/research could evolve to encompass the realities of swiftly-changing politics, social norms, and polycrisis. Opening key knowledge gaps, provocateurs reflect research on what is missing and how to move new insights forward. Sandra Waddock (Boston College) introduces topics and speakers then asks what next in the light of global challenges shifting democratic and egalitarian norms? What now and how to catalyze transformative action? Brad Agle (Brigham Young) discusses The Totality of Allegiance: Inside the Hearts and Minds of Trump Supporters (with Travis Ruddle), which researched how so many good, honest, hardworking Americans could support a politician who clearly did not share many of their values or personal conduct. Irene Henriques (York U) explores how the massive increase in information availability and growing specialization of media outlets has transformed stakeholder perceptions of when their interests and identities are at risk or could be advanced by influencing firm behavior. Otto Scharmer (MIT) introduces Presencing: Seven Practices for Transforming Self, Society, and Business (with Kaufer), and the concept of social soil, arguing that democracy is undermined by two main forces: dark money and ‘dark tech’. Bobby Banerjee (City St George’s, U London) explores key aspects of Project 2025 – a US-manifesto so radical and regressive that even Donald Trump tried to distance himself from it. These topics and numerous others based on audience input arguably need to be incorporated into management thinking, paradigms, and research to evolve a world that copes with today’s emerging polycrisis.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".