Bibliographic record
Abstract
Novel molecular interventions have recently shown the potential to erase, enhance and alter specific long-term memories. Unique features of this form of memory modification call for a close examination of its possible applications. While there have been discussions of the ethics of memory modification in the literature, molecular memory modification (MMM) can provide special insights. Previously raised ethical concerns regarding memory enhancement, such as safety issues, the 'duty to remember', selfhood and personal identity, require re-evaluation in light of MMM. As a technology that exploits the brain's updating processes, MMM helps correct the common misconception that memory is a static entity by demonstrating how memory is plastic and subject to revision even in the absence of external manipulation. Furthermore, while putatively safer than other speculative technologies because of its high specificity, MMM raises notable safety issues, including potential insidious effects on the agent's emotions and personal identity. Nonetheless, MMM possesses characteristics of a more permissible form of modification, not only because it is theoretically safer, but because its unique mechanism of action requires a heightened level of cooperation from the agent. Discussions of memory modification must consider the specific mechanisms of action, which can alter the weight and relevance of various ethical concerns. MMM also highlights the need for conceptual accuracy regarding the term 'enhancement'; this umbrella term will have to be differentiated as new technologies are applied to a widening array of purposes.
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.
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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".