Strategy making, novelty and analogical reasoning — commentary on Gavetti, Levinthal, and Rivkin (2005)
Bibliographic record
Abstract
Abstract This commentary responds to and builds upon a recent article about the role of analogical reasoning in strategy making (Gavetti, Levinthal, and Rivkin, 2005). Based on conceptual and formal analysis, the authors state that in complex and novel contexts, analogical reasoning may be superior to two established models: rational choice and local incremental search. I show that given an alternative conceptualization of the strategy‐making context and main models, analogical reasoning is not necessarily superior. Furthermore, in novel and complex contexts, this model and other approaches such as mental experimentation can play a larger role, particularly in inventing effective strategies. I further extend the analysis by considering some boundary conditions in which analogical reasoning and its alternatives best apply, exploring the idea that blending and adapting several search strategies may be more effective than using only one method, such as analogical reasoning, and advancing new directions for empirical research. Copyright © 2008 John Wiley & Sons, Ltd.
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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.028 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 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".