Mobility and its effect on scientific recognition. A prosopographic analysis of Swiss biologists
Bibliographic record
Abstract
This article aims at understanding the biographical dynamics of mobility—academic, institutional, geographic, and disciplinary—and its effect on scientific recognition. We draw on a comprehensive data collection on career progression, publications, and funding for all biology professors in Switzerland active between 2008 and 2020. Data sources combine CV information, data from the Web of Science and the Swiss National Science Foundation. Thanks to multiple-sequence analysis, we are able to consider six career types and their effect on scientific recognition. Our main finding is that different combinations of mobility have different effects on scientific recognition. Disciplinary mobility, however, has a very limited effect on shaping scientific careers, although we also observe a positive effect of disciplinary mobility in cases when it occurs early in the career. Professors who became interdisciplinary very early are also those who are the youngest at tenure and who benefit from the highest level of citations when considering their entire career. Because the effects of mobility on career success depend on specific combinations of academic, geographic, institutional, and disciplinary mobility, as well as ascriptive characteristics, we argue that biographical process should be considered in studies on scientific careers. • This article proposes a cross-fertilization of bibliometrics and scientific careers. • Focus on combinations of academic, institutional, geographic and disciplinary mobility • Effect on scientific recognition depends on different combinations of mobility. • Organizational and international mobility matter more than disciplinary mobility. • Early-career disciplinarity mobility has a positive effect on scientific recognition.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".