Orwellian Politics for the Age of Algorithms: Interrogating Human Engagement with AI
Bibliographic record
Abstract
“It is not impossible to build a human brain and we can do it in 10 years”, said Henry Markram in the TED Global conference in Oxford in 2009 (Fildes2009). Henry Markram is the director of the Blue Brain Project, which aims to reverse-engineer the mammalian brain from laboratory data. He explained, “It's a new brain. The mammals needed it because they had to cope with parenthood, social interactions and complex cognitive functions”(Fildes 2009). As predicted, AI has indeed come to dominate the predicted timeframe. Today, those who have lived through the first quarter of the twenty-first century have already witnessed the rise of technology and digitalization to the extent of overpowering humankind. We once lived in a world connected by the World Wide Web and now, the world is defined by the interconnectedness of human species, nature, and technology. This renewed survival dynamic also refines the concepts of power, survival, and crisis in this third millennium.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.055 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| 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".