Early Career Researchers' Quest for Reputation in the Digital 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.140 | 0.477 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.036 | 0.080 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.301 | 0.091 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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