Is the year of first publication a good proxy of scholars’ academic age?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it