On docility: a research note on Herbert Simon’s social learning theory
Bibliographic record
Abstract
Purpose – The purpose of this paper is to address the core concept of docility in Simon’s learning theories and elaborate docility as a missing link in organizational performance structures. In his book,Administrative Behavior, first published in 1947 with three subsequent editions, Herbert A. Simon introduced a new concept to the emerging field of organizational theory, docility. Design/methodology/approach – InAdministrative Behavior, Herbert A. Simon introduced to management and organization theorists the concept of docility. Simon adopted the concept and meaning from E.C. Tolman’s (1932) classic work,Purposive Behavior in Animals and Men, and his novel views on learning processes and key concepts like purpose (goals), thought processes (cognitive psychology) and cognitive maps. This paper elaborates on docility mechanisms and the implications for social learning in organizations. Findings – This paper addresses this lacuna in the organizational literature, and the implications for current theories of organizations and organizational learning. Practical implications – Docility is a tool to link individual learning with organizational learning in complex environments and changing technologies. Originality/value – The paper traces origins of Simon’s docility and learning theories.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".