Chinese Early-Career Researchers' Scholarly Communication Attitudes and Behaviours: Changes Observed in Year Two of a Longitudinal Study
Bibliographic record
Abstract
This paper presents research into the scholarly communication attitudes and behaviours of Chinese early-career researchers (ECRs). This research comes from year two of a projected three-year-long study of ECRs from seven countries (China, France, Malaysia, Poland, Spain, the UK, and the US), for which semi-structured in-depth interviews were conducted with study participants. For the findings reported in this paper, fourteen Chinese ECRs from science and social science disciplines at six different universities were interviewed during the period from March to May 2017. The interview record was compared with the previous year's (2016) record to identify changes in interviewees' responses to a battery of questions. In addition, contextual data were obtained from the CVs of the ECRs. Our findings indicate that the scholarly communication attitudes and behaviours of Chinese ECRs have changed from year one to year two. We observed noteworthy changes in Chinese ECRs' attitudes and behaviours regarding open access publishing and peer review. As compared with data from 2016, the ECRs are more positive about open access journals but more negative about the peer-review system. Social media and online communities are now more frequently used as supplementary channels for scholarly communication, and WeChat is becoming very popular for Chinese ECRs. Authorship policies, the academic evaluation system, and the prevalent use of social media are the most important factors bringing about the changes we observed. What remains unchanged for the Chinese ECRs is the persistent pressure they feel to publish papers in select journals in order to advance their careers.
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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 | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Scholarly communication Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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.
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".