The Use of Online Strategies and Social Media for Research Dissemination in Education
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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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 | 0.085 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".