Bibliographic record
Abstract
Online social networks have made communication more accessible in many spheres; they have been used as an alternative political means of regaining or equalising power between a political opposition and a ruling party, when the former is functioning in the absence of a traditional political platform. Recently opposition politicians in Tanzania have utilised online social networks towards this end. Positioning theory draws out this political dynamic in operation. Historically, political communication using electronically transmitted networks in Tanzania was necessitated after the fifth-phase presidency (beginning in 2015) placed a ban on political activities reaching outside their official jurisdictions and naming specific candidates. This ban seemed to weaken opposition politicians because they were regarded as preferring to work in collectives. The analysis here focuses on a press conference involving a United Republic of Tanzania Member of Parliament from the opposition party, addressing the national and international communities in regard to secrecy that surrounded a late arrival of two new jets into Tanzania from Canada, late in 2016. The data suggests that online social networking has enabled opposition politicians to identify themselves as fellow sufferers and representatives of the ordinary citizen, demanding good governance and speaking against misappropriation and laxity in distribution and use of national resources. The opposition has gone further in utilising social media to present the nation’s presidential agenda as pitted against the ordinary citizen. Social media allows the opposition to represent the current government as an elite group responsible for the problems Tanzanians are facing, and therefore as untrustworthy. This limited case study reveals how electronic media re-introduces a potential for effective political opposition to the status quo at a national level.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".