My Presidency of the Academy of Management: Moral Responsibility, Leadership, Governance, Organizational Change, and Strategy
Bibliographic record
Abstract
I was the President of the Academy of Management (AOM) in 2016-2017 when U.S. President Donald Trump issued an Executive Order banning immigration and travel to the United States by citizens of seven predominantly Muslim countries (EO13769). While I immediately sought to condemn EO13769 as immoral and as a threat to the AOM, I was only able to issue a condemnation in my own name and not in the name of the AOM because the Board’s Executive Committee correctly determined that a condemnation would have violated the AOM Constitution. This put me in the untenable position of leading an organization operating under principles that conflicted with my personal beliefs about an immoral act of government. The article is a case study on this situation. In it, I explain how EO13769 and other attacks on science threaten the purpose and functioning of the AOM. The case explores a relatively understudied aspect of leadership: the identity of an organization as distinct from the identity of its leader. It also underscores the importance of strengthening democratic institutions of science. I argue that the issuance of statements of condemnation—while important—does not exhaust our responsibilities in society as scholars for investigating, reporting, defending, and protecting the truth about what is going on in the world around us. I conclude by calling us to redouble our commitment to a defining purpose of the AOM, which is to support the scholarship necessary to overcome polarizing politicization of complex social issues.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.025 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".