Scholars’ temporal participation on, temporary disengagement from, and return to Twitter
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
Even though the extant literature investigates how and why academics use social media, much less is known about academics’ temporal patterns of social media use. This mixed methods study provides a first-of-its-kind investigation into temporal social media use. In particular, we study how academics’ use of Twitter varies over time and examine the reasons why academics temporarily disengage and return to the social media platform. We employ data mining methods to identify a sample of academics on Twitter (n = 3,996) and retrieve the tweets they posted (n = 9,025,127). We analyze quantitative data using descriptive and inferential statistics, and qualitative data using the constant comparative approach. Results show that Twitter use is predominantly connected to traditional work hours and is well-integrated into academics’ professional endeavors, suggesting that professional use of Twitter has become “ordinary.” Though scholars rarely announce their departure from or return to Twitter, approximately half of this study’s participants took some kind of a break from Twitter. Although users returned to Twitter for both professional and personal reasons, conferences and workshops were found to be significant events stimulating the return of academic users.
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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 | 0.001 | 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