Bibliographic record
Abstract
As predictive technologies begin to extend past business and into government and culture, it is important to analyze what it is that they are providing. Often, these technologies are understood as telling us stories about what is going to happen – they attempt to demystify some aspect of the future. Yet, data – the grounds for all of these technologies – do not come with any context with which to situate a narrative. What then are predictive technologies giving us? In this essay, I use Walter Benjamin’s analysis of storytelling to develop a model of the relationship between ‘narratives’ and ‘data’. He describes a move away from storytelling in the early 20th century and into information, which I then extend to data and big data; while the core aspects of narrative are shucked away with this descension, we somehow find that predictive analytics appear to be telling stories through data. However, through an analysis of the relative capacities of both computer processes and humans capacities in relation to the concept of ‘council’ – a term here used to delineate suggested future courses of action in relation to some kind of predictive scheme - I argue that these predictions do not have the same implications that stories do. Rather, they take patterns from the past and apply them as projections toward the future, which, taken uncritically, means the reiteration of past patterns in the future. These technologies therefore create a kind of temporal uniformity that prevents change or serious derivation from the past. Defaulting to the decisions issued by predictive software, then, seriously hinders the possibility for different futures – the possibility that something genuinely novel could occur.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".