From Expression to Expulsion: Digital Public Spaces as Theatres of Operations in Nepal
Bibliographic record
Abstract
On 2 May 2016, Robert Penner, Canadian national residing in Nepal with a working visa, was arrested and then deported to Canada and his visa being cancelled. Based upon an analysis of the documentation related to his arrest and expulsion, this article analyses the articulation of different operations of control. A chain of public interventions and governmental actions makes the substance of the management of digital expression in Nepal and this has to be analysed with tools from media studies and science and technology studies. We present different operative regimes: Twitter accounts and discussions, police action and arrests, and court petitions. We analyse how operational levels are connected and how their interconnections lead to the criminalisation of one individual, most notably through the reformulations of the accusations by different groups of people via different devices. This in turn shows how specific technical interventions determine the control of the public space. These analyses then add to the debate upon the ‘digital public sphere’ by offering a critique of its spatial metaphor from a view focused on its performative stakes—public spaces not as sites of discussion, but as theatres of operations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".