Promoting intentional unlearning through an unlearning cycle
Bibliographic record
Abstract
Purpose Although there is widespread agreement about the importance of and need for unlearning particularly in an organizational context, concerns have been expressed by some researchers with respect to the coherence of the concept. The purpose of this paper is to complement organizational theories of unlearning with a clearer definition of intentional unlearning and develops an “unlearning cycle” comprising of the steps that influence unlearning focused on the need to update knowledge obtained in the past. Design/methodology/approach In this paper, the authors review both the current state of conceptual development and the empirical underpinning of the concept of unlearning and relate it to emerging literature on the links between levels of learning to then propose a conceptual framework which includes employees and managers as key actors in enabling intentional unlearning. Findings Unlearning critics have argued that unlearning has no explanatory value and is unnecessary because clear alternatives and less problematic concepts better frame the research gap that has been identified in the unlearning research literature. By addressing these concerns, this study proposes three key structures to facilitate intentional unlearning, namely, those represented by the unlearning cycle. Originality/value This study sheds light on the relationship across different unlearning levels. In addition, this study attempts to indicate how greater rigor may be brought to the development of research in the fields of intentional unlearning.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".