Bibliographic record
Abstract
Technology is the driving force within society and is the single most important factor affecting all human endeavors. If we think about what is happening with expanding technology in communications, we realize that within minutes of the World Trade Center attacks, televisions all over the world were informing local populations of the incident. We have cellular phones and an ability to communicate with anyone anywhere from any place. We have access to information through the internet to products that we never dreamed were accessible. There is increased efficiency in business. There is merchandising control in industry, and the distribution and sale of goods is enormously facilitated with far less redundant warehousing of products. Farming has been tremendously enhanced. We have genetically altered insect-resistant crops. There is mass production and processing of food including freezing, preserving, and even manufacture of artificial foods. Travel has been augmented in an almost unbelievable manner in the past 50 years with worldwide travel, space travel, and weather information. Within the military, global positioning of missiles has freed the need for manpower to deliver arms and drone, pilotless spy planes have allowed information gathering without endangering personnel. There are computer-based, remote educational opportunities. People can actually earn
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.959 | 0.927 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".