The political affordances of the ‘coconut wireless’: Rotumans on social media in the 2018 Fiji elections
Bibliographic record
Abstract
As a unique group of people, Rotumans make up less than two percent of Fiji’s population, and as a minority Indigenous ethnic group in Fiji, they have remained relatively hidden and silent in political affairs. Outmigration from the island has led to more than 80 percent of Rotumans residing outside of Rotuma. In recent times, the Rotuman diaspora has heavily relied on the use of ICTs and new media technologies as crucial tools for the reinvigoration of Rotuma’s culture. This in itself poses an intriguing paradox as internet connectivity on Rotuma is quite limited. However, social media platforms have been increasingly used by Rotumans outside of Rotuma, and have enabled increased connectivity and greater dissemination of information among the Rotuman diaspora. Recently, the primary purpose of such social media groups has evolved from merely being a tool for rekindling familial ties, to being a platform for political discourse on Rotuman issues. In essence, despite the scattered nature of the Rotuman population, digital technologies are offering Rotumans the affordance of being able to inform and educate themselves and their networks on political issues of Rotuman interest. By employing ethnography and netnography principles and through in-person and online engagement with Rotumans within and outside of Rotuma, this article examines the affordances that digital technologies offer Rotumans concerning national political discourse. This is carried out with a specific focus on the 2018 general elections in Fiji.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".