Bibliographic record
Abstract
In March 2018, it was revealed that Cambridge Analytica (CA), a former United Kingdom-based data company used data from several million Facebook users to specifically target individuals with political ads. CA’s data mining operation can be argued to have engaged in restructuring power through the online discourse between people and groups, granting certain actors and their movements increased power. This reflects a shift to the 5th generation of warfare. 5G warfare, as it’s colloquially known, is the assumption that groups vie for power against other groups, and not necessarily the state. Furthermore, 5G warfare is enabled by shifts of political and social loyalties to causes rather than nations (Kelshall, 2018). Indeed, warfare has become virtual and seeks to influence people, and not states. Through CA’s use of psychographic research and its ability to reshape the opinions of the public, power has shifted from the physical to the digital, and from the state to the people. Therefore, the question this essay presents is “How did Cambridge Analytica make power available to those who did not otherwise have it?”
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.042 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".