Bibliographic record
Abstract
This paper argues that research in Buddhism must have a soteriological focus. To demonstrate this, an overview of the Cūḷamālunkya Sutta (MN 63) is presented. This sutta consists of a conversation between the Buddha and one of his students, and it reveals that Buddhism’s topics of inquiry must address how one can be free from suffering. The implication of this conversation – the soteriological focus – seems to suggest that Buddhist research excludes topics in metaphysics, such as addressing the nature of the universe (if it has a beginning or an end, if it is finite or infinite, and so forth), or the nature of the self. Soteriology seems to suggest that ethics is the only focus of research in Buddhism; that is, to know how to be free from suffering, one must study how one should live and conduct oneself. Though this appears to be the case, this paper will show that research in Buddhism is not limited in this manner. Instead of excluding metaphysical research entirely, Buddhism instead excludes research that is done for its own sake; topics must therefore be researched for the sake of soteriology. Thus, the research implication of the Cūḷamālunkya Sutta is not that certain topics are unable to be researched, but rather that a qualification of soteriology is attached to topics of research.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".