Bibliometric Analysis of <i>Human Factors</i> (1970-2000): A Quantitative Description of Scientific Impact
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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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.130 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".