The ‘Art’ of Science and Research: Jabir Ibn Hayyan Laid the Foundation
Bibliographic record
Abstract
This article identifies scientists' attributes and their approaches to innovation, sciences, research, and discovery as ascribed by Abu Musa-Jabir Ibn Hayyan al- Azdi - also known as Jabir Ibn Hayyan (or Geber) in the late 7th to early 8th century. Jabir was the first polymath to have set the stage for the Golden Age of Islam that lasted from the 8th to 12th century. In several of his books and research articles, Jabir identified researchers, scientists and scholars as the “artists” and their research methodologies and experimentation as the "art." A mastery or specialization in any given discipline that an "artist' pursues was termed by him as the “Majistery”. The attributes that he proposed several centuries ago have since become the criteria, befitting the “art” of our present-day scientists and scholars. He explicitly detailed the attributes of an “artist” and also those who were recommended not to pursue sciences as a career. He described natural talent, innate propensity, the conquest of knowledge, deeper insights into Mother Nature, ingenuity, critical thinking, foresight, flexibility, adaptability, resiliency, persistence and selflessness as the essential ingredients of scientists and their success. Additionally, he also deemed funding, collaboration, partnership and community support to be pivotal. Rigidity – the "stiff neck," as he described it, and the lack of adaptability to be detriments to the ‘art’ of sciences. This article provides an eye-opening account of the scientific rigor that led to the Golden Age on the one hand, and on the other hand, attempts to reconcile the compatibility of modern sciences with traditional Islamic teachings. It also identifies the critical success factors that led to the rise of sciences in the Islamic world, which have since either been forgotten or ignored. We make recommendations throughout as to what needs to be done to revive the Golden Age of Sciences in the Muslim world.
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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".