From organizational learning to organizational mnemonics: Redrawing the boundaries of the field
Bibliographic record
Abstract
In this article, we advocate for a more balanced approach to the study of the past in management and organization studies. We define organizational mnemonics as a broader field of inquiry focused on theorizing the past as an integral part of organizational life, including three major epistemic communities—that is, functionalist, interpretive, and critical. We contend that much of organizational mnemonics research has been dominated by functionalism, at the expense of other approaches. To remediate this situation, we first characterize organizational mnemonics’ core epistemic communities. Second, we look at the boundary work at the interstices of these communities to explore possibilities of dialogue among them. We argue that the future of the study of the past in organizations should acknowledge different perspectives, the intersections among them, and make a conscientious effort to maintain diversity of scholarship in the field.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.016 | 0.039 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".