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
This paper will take the stance that cognitive enhancement promised by the use of AI could be a first step for some in bringing about moral enhancement. It will take a further step in questioning whether moral enhancement using AI could lead to moral and or religious conversion, i.e., a change in direction or behaviour reflecting changed thinking about moral or religious convictions and purpose in life. One challenge is that improved cognition leading to better moral thinking is not always sufficient to motivate a person towards the change in behaviour demanded. While some think moral bioenhancement should be imposed if necessary in urgent situations, most religions today see volition in conversion as essential. Moral and religious conversion should be voluntary and not imposed, and recent studies that show possible dangers of the use of AI here will be discussed along with a recommendation that there be regulatory requirements to counteract manipulation. It is, however, recognized that a change in moral thinking is usually a necessary step in the process of conversion and this paper concludes that voluntary, safe use of AI to help bring that about would be ethically acceptable.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it