A Social Media Research Network Framework for Open Social Scholarship
Bibliographic record
Abstract
Traditional research impact factors, researcherIDs and alternative metrics can be used together to provide a more comprehensive measure of the impact of wider critical reflection around technology enhanced learning (TEL) research and practice. Alongside this there is a growing global community of educational researchers that are actively engaging in the scholarship of technology enhanced learning (SOTEL), sharing experiences via open publishing formats and social media (Costa, 2014; Littlejohn, Beetham, & McGill, 2012). Many academic institution libraries provide guides for researchers to increase their research impact via researcherIDs and social media, for example: (Orr & Blinstrub, 2015). However this is a fairly passive approach to changing academic research culture. In this paper we explore ways of facilitating a discussion around academic research culture change through the intersection of Educational Design Research (EDR), Alternative metrics, SOTEL, open scholarship, and social media. We introduce an open social scholarship community model we are using to support academics develop a culture and network around open social scholarship.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".