What's in a Name? “ESCA” or “XPS”? A Discussion of Comments Made by Kai Siegbahn More Than Four Decades Ago Regarding the Name of the Technique
Bibliographic record
Abstract
ABSTRACT In an interview in 1982, which was 1 year after he shared the Nobel Prize, Kai Siegbahn was asked about his opinion regarding the name of the technique he had developed. Siegbahn had named it “electron spectroscopy for chemical analysis” (ESCA), but the community was choosing to call it “X‐ray photoelectron spectroscopy” (XPS). Now, more than 40 years later, 20 XPS experts have given their opinions on Siegbahn's response and the name of the technique. Some of these participants have been doing XPS for many years and have provided a historical perspective on this issue. While there is no call in these comments for the community to return to “ESCA”—“XPS” is regarded as a more than an adequate name, and insisting on a name change at this point in time would probably only create confusion. However, some of the participants of this study still consider “ESCA” to be an acceptable way to refer to the technique, especially when it is used in a chemical context.
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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 | 0.051 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".