Gayle Rogers, <i>Speculation: A Cultural History from Aristotle to AI</i>
Bibliographic record
Abstract
Speculative thinking gets a lot of pushback, today.The idea that we are capable of knowing the future ahead of time-contained, for instance, in the Stalinian concept of being on the so-called right side of history-has, today, in the wake of the disaster of twentieth-century totalitarianisms, been pushed aside in favor of present thinking and immediacy.The notion that we can, in any way, know the proper or correct direction of History probably seems absurd to many of us.Nevertheless, teleological thinking, if understood more as a speculative process than as direct insight into the objective laws of History, is a component part of so many aspects of our social, cultural, economic, and political organizing.Even the Freudian concept of Nachträglichkeit (or retroactivity), Hegel's Owl of Minerva, or the science fiction genres of utopia and dystopia, involve a dimension of teleological (or purposive) speculative thinking, which, regardless, involves a framework for thinking the present.Projecting potential outcomes (or thinking the present as an outcome of past action) provides a strategy for determining practical action in the here and now.It helps us to come to terms with the essential question: What is to be done?Speculative thinking is also driven by its counterparts: risk and uncertainty.Speculation, no less than teleological thinking, can, in this way, be seen as a mechanism or strategy for the reduction of risk and uncertainty, to control or gain insight into the future, by determining present action.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; both teacher heads agree on what is shown here.
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".