Is the year of first publication a good proxy of scholars’ academic age?
Bibliographic record
Abstract
Individual scholars are the central unit of the research system and are increasingly the focus of bibliometric studies. An important aspect in the study of individual scholars is their academic age, which allows for the comparison of scholars that have been academically active in a similar period of time. Based on a sample of Quebec researchers for whom their year of birth, PhD year as well as the year of their first publication are known, we study the relationships among these ages with the aim of determining how their year of first publication can be used to estimate their ‘real’ age. Moderate correlations have been found among the ages, and the first publication year has a higher correlation with the PhD year than with the birth year. However, an important dispersion of scholars across the different ages is observed; thus, the year of first publication can only be taken as proxy of the real age of scholars. Alternatively, the consideration of cohorts of around 5 years seems to be a reasonable approach. Further research will focus on the exploration of other bibliometric indicators in order to refine the preliminary developments discussed here. Conference Topic Methods and techniques Introduction In individual-level bibliometric studies, the socio-demographic characteristics of scholars are of central importance to understand and better frame the results obtained (Costas & Bordons, 2011; Gingras, Lariviere, Macaluso, & Robitaille, 2008; Mauleon & Bordons, 2006). Among these socio-demographic characteristics we can mention gender (Lariviere, Ni, Gingras, Cronin, & Sugimoto, 2013; Mauleon & Bordons, 2006), mobility (Canibano, Otamendy, & Solis, 2011; Franzoni, Scellato, & Stephan, 2012), and nationality (Moed & Halevi, 2014), among others. The development of large-scale author-name disambiguation algorithms (Caron & Van Eck, 2014) as well as the increasing quantity of papers’ metadata indexed (e.g. author names and surnames, affiliations, e-mail data, etc.) have allowed the study of the socio-demographic characteristics of scholars at a larger scale. For example, the analysis of the first author names of authors (Lariviere et al., 2013) allowed the macro analysis of gender disparities worldwide. The large-scale analysis of the relationship between author names, affiliations and countries collected from scientific publications has open the possibility of studying academic mobility at the world level (Moed, Aisati, & Plume, 2013), as well as the nationality (Costas & Noyons, 2013), migrations (Moed & Halevi, 2014) or even the ethnic origin (Freeman, 2014) of scholars. A critical element for individual-level bibliometrics is the age of the researchers (Costas & Bordons, 2011; Lariviere, Archambault, & Gingras, 2008; Levin & Stephan, 1989), especially from the point of view of its relationship with productivity (Falagas, Ierodiakonou, & Alexiou, 2008; Levin & Stephan, 1989). Age is also a common point of debate in science policy, as it aims to compare scholars of the same ‘academic age’ (Bornmann & Leydesdorff,
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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 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".