Bibliographic record
Abstract
It always begins innocently enough! In the middle of the 19th century, mining and earthmoving were increasingly important enterprises of the industrial revolution. To remove rock and to open mine shafts, an explosive was needed, but nitroglycerine was too unstable for practical use. The Swedish scientist/inventor Alfred Nobel discovered that mixing nitroglycerine with the diatomaceous earth kieselguhr produced a stable explosive product he patented as dynamite, which was quickly adopted by the mining and construction industries. In the early 20th century, the Italian physicist Enrico Fermi, while attempting to understand the structure of atomic nuclei, discovered that nuclei bombarded by neutrons would split and release large amounts of energy. As others have employed these discoveries, both dynamite and nuclear fission have had destructive effects on society that were initially unimaginable by their discoverers. It was only a quarter century after the first nuclear fission bombs that Eugene Garfield, a library scientist and structural linguist from the University of Pennsylvania, discovered a metric that could be used to select journals for inclusion in his new publication Genetics Citation Index (the forerunner of Science Citation Index, which was subsequently commercialized by Garfield’s company Institute for Scientific Information). This metric for journals was named “impact factor” and was to be calculated “based on 2 elements: the numerator, which is the number of citations in the current year to any items published in a journal in the previous 2 years, and the denominator, which is the number of substantive articles (source items) published in the same 2 years.” 1,2 Thus, although the journal impact factor was born innocently enough, just like the examples involving Nobel and Fermi, Garfield’s impact factor is now being used by others in ways that threaten to destroy scientific inquiry as we know it. 3,4 For much of human history (about 200,000 generations), scientists were few in number, often worked in relative isolation, and only communicated findings to close
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 | 0.036 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".