Bibliographic record
Abstract
A Behavioral Theory of the Firm (Cyert & March, 1963) is a multifaceted collection of “little ideas” that has the potential to evolve into multiple and potentially revolutionary directions. In my first encounters with the BTF (the book and the theoretical research program), I was intrigued by its predominant focus on models of dynamic processes. BTF employs a distinctive style of theory construction that proceeds through articulation of models of behavior in organizations, and that builds on relatively parsimonious and realistic assumptions about underlying organizational processes (of decision making, information processing, expectation formation, etc.) and their limitations (arising from bounded rationality of organizational actors and processes). It is a bold approach of modeling the organizational processes that shape action in and of organizations, and it has been a persistent source of inspiration for many students of organizations since then. At the same time, BTF models tend to be dynamic (often incorporate computer programs that simulate dynamic processes) and can reveal the surprising implications of organizational processes and structures (e.g., competency traps, superstitious learning, garbage cans, etc.) and their unfolding in time. In particular, dynamic models of organizational learning and organizational rules appear to offer unusual potential for the future elaboration of the BTF.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| 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".