MNEMONIC CAPABILITIES: COLLECTIVE MEMORY AS A DYNAMIC CAPABILITY
Bibliographic record
Abstract
ABSTRACT Dynamic capabilities (DCs) are the processes that organizations develop to remain competitive over time. However, in spite of the importance of temporality in the development of DCs, the roles of time, history, and memory remain largely implicit. In fact, most studies focus on the past as a source of constraints and limits for managerial action. Alternatively, we advocate for a social constructionist view of the past. Our core argument is that the capacity to manage the past is a critical competence of modern organizations. We argue that organizations can manage their collective memory as resources that aid the objective reproduction and exploitation of existing routines, the interpretive reconstruction and recombination of past capabilities for adaptation to environmental change, and the imaginative extension and exploration of collective memory for anticipated scenarios and outcomes. This renewed view of time, history, and memory is better suited for a dynamic theory of competitive advantage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".