Anarchists, pirates, ideologists, and disasters: New digital trends and their impacts
Bibliographic record
Abstract
Abstract This panel will address both online disasters created by anarchists and pirates and disaster relief efforts aided by information and communication technologies (ICTs). An increasing number of people use (ICTs) to mobilize their resources and enhance their activities. This mobilization has unpredictable consequences for society: On one hand, use of ICT has allowed for the mobilization of millions of people for disaster relief efforts and peace movements. On the other hand, it has also helped hackers, pirates to carryout destructive activities. In many cases it is hard to judge the moral consequences of the use of ICT by marginalized groups. The panel will present five studies of which three will focus on online disobedience and two will focus on ICT use for disaster. Together these presentations illustrate both positive and negative consequences of the new digital trends. Goodrum deliberates on an ethic of hacktivism in the context of online activism. Eschenfelder discusses user modification of or resistance to technological protection measures. Shachaf and Hara present a study of anarchists who attack information posted on Wikipedia and modify the content by deleting, renaming, reinterpreting, and recreating information according to their ideologies. Scott examines consumer media behaviors after hurricane Katrina and Rita disasters. Shankar and Ozakca discuss volunteer efforts in the aftermath of hurricane Katrina.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".