Early Career Researchers' Quest for Reputation in the Digital Age
Bibliographic record
Abstract
The purpose of this article is twofold: a) to describe and compare methods of early career researcher (ECR) assessment/appraisal; b) to explain how ECRs build, showcase, and monitor their reputation in an era of novel developments in scholarly communications. In all, 116 ECRs from China, France, Malaysia, Poland, Spain, the UK, and the US were questioned about appraisal and reputation in structured in-depth interviews. Desk research supplemented the interview data. It was found that ECRs are assessed very traditionally, largely on journal papers, and cannot (although some would like to) see this state of affairs changing. Mainly, they would prefer that less weight be given to the volume of papers published and more weight given to the quality of their research and its impact on the body of knowledge in their field. Unavoidably, then, ECRs' efforts to build, showcase, and monitor their reputation are still very much associated with research achievements. Nevertheless, online scholarly communities, and ResearchGate in particular, are gaining ground among ECRs, with increase in visibility and citations, and therefore a maximization of research impact, considered to be their main reputational benefits. Metrics are regarded as ‘a rule of the game' that has to be accepted, although ECRs have minimal interest in altmetrics.
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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 | 0.078 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".