Toward a General Theory of the Organization: From Cells to Societies
Bibliographic record
Abstract
A central theme of organization studies is to understand how organizations composed of multiple actors each with different perspectives and motivations achieve coordination (March & Simon, 1958; Thompson, 1967; Mintzberg, 1980; Puranam, 2018). Whether it's the structured hierarchies within bureaucratic organizations (Weber, 1978; Monteiro & Adler, 2022) or self-managing entities without direct managerial control (Ashby, 1947; Lee & Edmondson, 2017), coordination is an essential, albeit additional, function beyond the primary operations of any organization. While organizational scholars have accumulated a substantial body of knowledge about coordination functions within human organizations, our symposium seeks to broaden the discussion to encompass a wider array of systems requiring coordination, where organizational scholars can derive novel insights. For instance, bee colonies, a remarkable super-organism that might seem to operate seamlessly, need coordination beyond genetic programming to adapt to the environment. Similarly, in bacteria, regulatory genes function akin to managers, orchestrating the activities within the cell. These examples from nature underscore the universality and importance of studying coordination processes beyond human society. Therefore, we brought presenters from a variety of fields to this symposium, each of whom highlights the systems and definition of coordination functions in organizations across diverse systems including firms, self-organizing system, biological systems, and federal agencies. While each system faces a different set of tasks or problems to be resolved, our symposium centers on developing a uni? ed science of coordination functions and its associated structure to answer the following questions: What are the driving factors behind the cost of the coordination? Can we predict the amount of regulatory costs an organism or organization needs based on its size, function, and complexity? Building on the theme of coordination across diverse systems, our symposium invites cross-disciplinary experts to mark a notable departure from the traditional themes at Academy of Management’s (AOM). This multidisciplinary dialogue integrates biological paradigms with organizational theory. We believe that this fresh perspective enriches AOM’s discourse, challenging its members to expand their analytical scope. This expansion is designed to deepen our collective grasp of organizational practices, which is aligned with the AOM’s dedication to the advancement of management sciences. Finally, our symposium is poised to cultivate an intellectual community that embraces and explores these innovative intersections. Managing formal organization hierarchy Author: Haochi Zhang; Bayes Business School (formerly Cass), City, U. of London What makes Individual I's a Collective We: Coordination mechanisms & cost Author: Jisung Yoon; KDI School of Public Policy and Management How much regulation does simple life need? Author: Chris Kempes; Santa Fe Institute Unifying regulatory costs across complex adaptive systems Author: Vicky Chuqiao Yang; Massachusetts Institute of Technology
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".