Taming Global Citizenship Education Within Twitter’s Attention Economy
Bibliographic record
Abstract
A contested concept that finds multiple theorisations and practices in relation to various ideological, geographical and cultural positionings, global citizenship education (GCE). has taken flight in the formal and non-formal education sectors over the past two decades, bringing together education-focused actors from government and civil society in dynamic relationships. With the proliferation of social media, GCE actors have taken to platforms such as Twitter for educational and communicative purposes, leading to the emergence of an attention economy surrounding GCE. This article utilises issue mapping to trace and visualise the performance of GCE by organisations in the Global North, comparing their formal organisational definitions with their communication of their GCE work over Twitter. While organisational public education and communications have long functioned within a competitive, neoliberal economy, this article focuses specifically on how the attention economy of Twitter contributes to the diffusion or capture of particular understandings of global citizenship through a GCE issue network.
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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 | 0.004 | 0.023 |
| 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 | 0.006 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".