Promoting intentional unlearning through an unlearning cycle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it