Academics’ Behavior on STM-Journal WeChat Public Account Platform: A Uses and Gratifications Perspective
Bibliographic record
Abstract
To explore the motives, uses, and gratifications of academics on the STM-journal WeChat public account (STM-journal WPA) platform, the authors conducted a survey by applying the uses and gratifications theory. The results showed that the primary motive of academics is to stay informed of the updates or progress in their field, and they derive the most satisfaction from the information-gathering capabilities of STM-journal WPAs. The uses of STM-journal WPAs varied with individual factors: both use frequency and duration per session were negatively correlated with academic ranks; female academics exhibit positive correlations with motives to meet social needs and derive gratification from social benefits; no statistical significance and apparent relation were detected between disciplines and use frequency and duration. These findings are both theoretically important to know social media for scholarly communication in China in the context of uses and gratifications theory and practically significant for managers of STM-journal WPAs seeking to serve their users more efficiently.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".