Long and Short-Term Impact of Papers in <i>Applied Spectroscopy</i>
Bibliographic record
Abstract
In 1955, Eugene Garfield introduced the concept of a journal impact factor as a metric for measuring the importance or influence of scholarly journals. These days a journal's fate is often tied strongly to the impact factor. It is a topic that comes up regularly and a source of concern for the journal because of the slavish focus on metrics in the publishing world and in the academic community. From our perspective, the impact factor is shown to be a poor metric for illustrating the long-term significance of papers published in Applied Spectroscopy. The five-year impact factor is a better indicator for the short-term impact of the papers published in this journal, while the cited half-life and the citing half-life both provide a better measure of the long-term impact of papers published in Applied Spectroscopy. Of the most highly cited papers published in this journal, those that describe innovative data processing techniques have been cited more than papers that describe specific applications of a given technique such as infrared (IR), Raman, or laser-induced breakdown spectroscopy (LIBS).
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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 | 0.022 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.055 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".