How Twitter is changing the meaning of scholarly impact and engagement: Implications for qualitative social work research
Bibliographic record
Abstract
Social media technologies continue to change the academic landscape. Twitter has become particularly popular in research arenas including social work and is being used for fieldwork, knowledge mobilization activities, advocacy, and professional networking. Although there has been some consideration of the benefits and risks of using social media in academia, little has been written from a qualitative social work perspective. Drawing on the example of Twitter, this article redresses this gap in the literature, by exploring how social media is changing the way research is conducted and promoted in relation to (1) measuring scholarly impact via altmetrics; (2) engaging with research participants; (3) networking and making collegial connections; and (4) advocating for social issues in the public realm. As we highlight tensions in each of these four areas, a key concern is how and for whom social media is contributing to the changing meaning of scholarly impact and engagement in research communities. We draw specific attention to how the inequalities that exist in academia writ large may be amplified on social media thus affecting overall engagement and perceived impact for researchers from marginalized social locations (e.g. gender, race, sexual orientation). We conclude by discussing specific implications of using social media in qualitative social work research and provide suggestions for future areas of inquiry.
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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 | 0.200 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.019 | 0.039 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".