Bibliographic record
Abstract
The Science and arts are commonly considered to be two vastly different disciplines contrasting each other primarily in terms of methodology. The sciences value precision and the ability to control and replicate results, while arts stem from the fluid and unique expression of one’s self using different mediums. However, despite these characteristics, the ultimate goal of both areas of study is to explore the unknown. For arts, this entails delving into human emotions through abstract thoughts and ideas, while sciences use the same abstractions and imagination to experiment with and create using objects in the natural world. This combination of internal and external explorations define the intrinsic elements of the universe, which is why arts and science belong together. Les sciences et les arts sont souvent considérés comme deux disciplines vastement différents qui se contrastent surtout en terme de méthodologie. Les sciences mettent en valeur la précision et la capacité de contrôler et répéter des résultats alors que les arts proviennent de l’expression fluide et unique de soi-même en utilisant de divers moyens. Cependant, malgré ces caractéristiques, le but ultime des deux domaines d’étude est d’explorer l’inconnu.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.021 |
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".