#DevResearch: Exploring Development Researchers Twitter Use for Research Dissemination
Bibliographic record
Abstract
Social media is increasingly used by researchers to discuss research and policy.However, little is known about access to social media as well as the nature of its use among development studies researchers. This study combines survey data on the social media use of 131 development researchers with data on 56,512 tweets by development researchers. Development researchers are most active on Twitter and Facebook, and use them to engage with academics and students. Twitter data reveal that only a small fraction of tweets explicitly discuss their country of residence. Implications for understanding the role of social media in the dissemination and use of development research are provided. Les chercheurs ont de plus en plus recours aux médias sociaux pour discuter de recherche et de politiques. Cependant, on en sait peu sur l’accès aux médias sociaux par les chercheurs en études du développement et leur utilisation de ceux-ci. Cet article combine les données provenant d’un sondage fait auprès de 131 chercheurs en développement et les données sur 56,512 Tweets envoyés par des chercheurs en développement. Ces chercheurs sont particulièrement actifs sur Twitter et Facebook, utilisant ces réseaux pour échanger avec des académiques et des étudiants. Cependant,les données sur Twitter révèlent qu’une part infime seulement des Tweets discutent explicitement de leur pays de résidence. Cette étude traite ainsi d’approches pour comprendre le rôle des médias sociaux dans la dissémination et l’utilisation de la recherche en développement.
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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 | 0.029 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".