The Use of Online Strategies and Social Media for Research Dissemination in Education
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
Alongside a growing interest in knowledge mobilization (trying to increase the connection between research, policy and practice) there has been a transformation of how knowledge is produced, accessed and disseminated in light of the internet and social media strategies. Few studies have explored the use of social media for research dissemination. This paper explores the online strategies used by 44 research brokering organizations (RBOs) in education across Canada. It is organized in four parts. The first provides a literature review of the terminology associated with Web 2.0 and social media as well as outlines the sparse empirical work that exists. The second presents empirical findings of online practices of 44 RBOs. The third section reports on the frequency of social media activity of RBOs as well as the nature of posts in order to ascertain whether or not research is actually being disseminated through these mechanisms. The final section discusses the implications of social media for research dissemination. Overall, use of additional online strategies by RBOs (other than websites) remains modest. Many of the strategies used are passive and do not allow two-way communication. Thirty percent of RBOs use social media; however, this usage in not pervasive and Facebook and Twitter networks are small. Other mechanisms to encourage active participation will be required alongside Web 2.0 and social media tools, if these strategies are to become robust avenues for knowledge mobilization and research dissemination.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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