Evaluating Non-profit Intraorganizational Twitter Use of UNICEF and MSF
Bibliographic record
Abstract
Non-profit organizations (NPOs) have used Twitter as a platform to communicate with their stakeholders since its launch in 2006, yet numerous studies show that there is no consistent strategy among organizations on how they communicate on the platform (Java et al., 2009). While these studies showcase some strategies employed by non profits on Twitter, there is a lack of research on how non profit organizations (NPOs) communicate on Twitter across branches of their organization in different regions. This pilot study evaluates 163 Tweets across Médecins Sans Frontières (MSF) and UNICEF Twitter accounts in Canada and South Africa to determine how NPOs frame their communications content on Twitter. Through an analysis of functional and rhetorical framing, the study discovers that some NPOs have consistent strategies in functional and rhetorical framing, while others are inconsistent between intraorganizational channels on Twitter. The findings also showcase the importance of critically looking at previous studies on NPO Twitter use, as studies that generalize how NPOs communicate on the platform may not match how NPOs communicate across their intraorganizational channels.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".