ECS-Ecrea Early Career Scholar Prize winner - An astrological genealogy of artificial intelligence: From ‘pseudo-sciences’ of divination to sciences of prediction
Bibliographic record
Abstract
Algorithmic media have adopted and adapted divinatory practices and vernaculars of prediction, prophecy, probability, fortune-telling and forecasting – suggesting a possible link between artificial intelligence and pre-scientific modes of speculation. Statistical thinking and magical thinking, too, can be recognised as closely correlated epistemological systems for governing societies and ways of life. In fact, primitive astrological practices of looking up at the stars may represent one of the earliest statistical projects involving sophisticated calculations and data sets. Such pattern-making techniques could even be considered precursory to machine learning. As a point of departure for exploring these eclectic relationships between stars and data, magic and machines, I use a media archaeological methodology to question the historical roles of both astrological and computational divination in mediating methods of control, surveillance and knowledge production across transforming societal contexts. This methodology is especially relevant for examining historical narratives in the field of cultural studies as it makes apparent the hyper-connectedness between objects, cultural representation and sites of hegemonic contention. My findings reveal relationships between celestial pattern recognition and efforts to exert control over and manipulate the natural environment and its populations, the historical impact of meteorological and climatological practices for predicting and influencing future events with artificial intelligence, and links between statistics and algorithmic data biases. This article suggests a speculative genealogy of astrology and artificial intelligence, as well as a genealogy of the theological, scientific and machinic unconscious.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".