Bibliographic record
Abstract
This paper applies John Locke's political philosophy to dissect the 2018 Facebook-Cambridge Analytica data scandal, revealing how social media's democratic promise can turn into a profit-driven, unjust digital governance. Locke's Two Treatises of Government serves as a theoretical lens to explore how social media sites, initially perceived as democratic spaces, can devolve into unjust and illegitimate digital governments. The contemporary moment inextrixibly intertwines the Internet and capital, resulting in profit as the driving force behind social media sites. This driving force thus biases digital giants, resulting in a dissonance between the percieved democratizing potential of the Internet and the reality of how these sites operate. By scrutinizing the breach of natural rights and the erosion of user trust, the paper argues that Facebook's actions create what Locke would define as a state of war between the platform and its users. The essay urges digital citizens to be aware of these dynamics so that there can be collective resistance against illegitimate digital governments. With this framework, digital citizens are given the tools to create just communities in the evolving digital lanscape.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".