Unlearning as an Integral Part of Knowledge Management: The Nature and Visualizations of the Process
Bibliographic record
Abstract
Information and knowledge are sacred words for information professionals. The idea of deliberately losing information or knowledge may seem counterintuitive to many of us. When we consider knowledge, we first of all focus on such “creative” processes as knowledge discovery, constructions, sharing, recycling, etc., and often skip the somewhat “negative” process of “forgetting”. Most people would say that we do not need to focus on the latter, that the nature of human cognition takes care of that. We need lots of efforts to learn, to memorize, and to build our knowledge while forgetting is easy. It just happens, and, unfortunately, much “more effectively” than we would like. But is it true? Is it really easy? L'information et la connaissance sont des mots sacrés pour les professionnels de l'information. L'idée de perdre délibérément de l'information ou de la connaissance peut sembler contre-intuitive pour beaucoup d'entre nous. Lorsque nous considérons les processus de la connaissance, nous pensons d'abord à ces processus «créatifs» que sont la découverte, la construction, le partage, le recyclage de connaissances, etc., et souvent nous sautons le processus quelque peu «négatif» de l’«oubli». La plupart des gens diraient que nous n’avons pas à nous concentrer sur ce dernier, car la nature de la cognition humaine en prend soin. Nous devons investir beaucoup d'efforts pour apprendre, pour mémoriser et pour construire nos connaissances, alors qu’oublier est facile. Cela se produit et, malheureusement, beaucoup « plus efficacement »que nous ne le voudrions. Mais est-ce exact ? Est-ce qu’oublier est vraiment aussi facile?
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".