Bibliographic record
Abstract
One of Hermann Hesse's masterpieces is Siddhartha (1922). Hesse won the Nobel Prize for literature in 1946. This book has nothing to do with science; it is a philosophical essay of the adventures of a man living at the time of the Buddha. Among the numerous extrapolations related to science that one can draw from this book, I selected 2 representative ones (1). The first relates to “wisdom,” which in our context of interest can be equated with “imagination.” Note that Govinda is Siddhartha's friend. Siddhartha says: “Wisdom is not expressible. Wisdom, when a wise man tries to express it, it always sounds like foolishness.” “Are you joking?” asked Govinda. “I am not joking. I am telling you what I have discovered. Knowledge can be expressed, but not wisdom. One can discover it, one can live it, one can be borne along with it, one can do miracles with it, but one cannot express it and teach it. This is what I already sensed as a youth, what drove me away from teachers.” Another aspect we teach our research graduate students is to first develop a hypothesis and then design experiments to prove or disprove the hypothesis. This “scientific method” has many important ramifications, because we all know that disproving a hypothesis is a kind of a disaster because the data would not usually be publishable and the time spent (sometimes years) would be wasted. Audacious hypotheses are attractive to highly successful scientists (who are not going to do the work but will share the glory) and rather dangerous for young investigators, as I have indicated before (2). It is also well known that numerous biology papers are not reproducible, mainly because many of us are obsessed with proving our hypothesis is correct (and publishing it), rather than showing it incorrect (and putting it on the shelf). Here is what Siddhartha says on the subject: Siddhartha says, “What should I have to tell you, venerable one? Perhaps that you seek overmuch? That you seek so much, you do not find?” “How is that?” asked Govinda. “When someone seeks,” says Siddhartha, “it can easily happen, but his eyes only see the thing he is seeking and that he is incapable of finding anything, incapable of taking anything in, because he is always only thinking about what he is seeking, because he has an object, a goal, because he is possessed by the goal. Seeking means having a goal, but finding means being free, open, having no goal. Perhaps you, venerable one, are indeed a seeker, for in striving after your goal, there is much you fail to see that is right before your eyes.”
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".