Bibliometric Analysis of <i>Human Factors</i> (1970-2000): A Quantitative Description of Scientific Impact
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bibliometric analyses use the citation history of scientific articles as data to measure scientific impact. This paper describes a bibliometric analysis of the 1682 papers and 2413 authors published in Human Factors from 1970 to 2000. The results show that Human Factors has substantial relative scientific influence, as measured by impact, immediacy, and half-life, exceeding the influence of comparable journals. Like other scientific disciplines, human factors research is a highly stratified activity. Most authors have published only one paper, and many papers are cited infrequently, if ever. A small number of authors account for a disproportionately large number of the papers published and citations received. However, the degree of stratification is not as extreme as in many other disciplines, possibly reflecting the diversity of the human factors discipline. A consistent trend of more authors per paper parallels a similar trend in other fields and may reflect the increasingly interdisciplinary nature of human factors research and a trend toward addressing human-technology interaction in more complex systems. Ten of the most influential papers from each of the last 3 decades illustrate trends in human factors research. Actual or potential applications of this research include considerations for the publication and distribution policy of Human Factors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it