Social Media Storytelling: Using Blogs and Twitter to Create a Community of Practice for Writing Scholarship
Bibliographic record
Abstract
This paper argues that social media can function as an informal community of practice in writing scholarship where knowledge is absorbed into a user’s identity and practice through storytelling. Social media has increasingly attracted academics and educators as a method of trialing new research ideas and classroom strategies, seeking early peer review, and as a knowledge translation strategy for sharing research findings. Platforms such as Twitter and blogs work in tandem to provide exposure, encourage reflection, and build community. Storytelling becomes a form of persuasion, through use of literary strategies, to influence change. This argument recognizes how social media writing is situated in a unique genre and requires writing strategies that may be unfamiliar to academic writers. A social media storytelling interlude demonstrates a case of social media persona development for writing scholarship and acts as an example of the voice, tone, and literary strategies of social media writing. The paper concludes with a discussion of strategies aligned with researching the impact of social media on pedagogical practices.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".