Implementing an unlearning approach to combat counter-knowledge in multiple sclerosis
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
In the social domain, the term counter-knowledge has been used to refer to misinformation, gossip, rumours, and conspiracy theories that masquerade as knowledge. An individual’s assimilation of such counter-knowledge can lead to inappropriate individual behaviours and organisational decision-making. This study proposes a framework for investigating the relationship between counter-knowledge and learning myopia at the individual level in the healthcare domain, focusing on Multiple Sclerosis (MS). Given that those suffering from MS can experience symptoms leading to both a slowing down of information processing and a limited capacity. It is argued that these symptoms are likely to lead to exacerbating other symptoms, such as anxiety and depression. In addition to investigating how social counter-knowledge results in individual counter-knowledge, the research proposes a framework for understanding the challenges of implementing machine unlearning approaches, and a set of strategies to disrupt this linkage is also proposed.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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