On the Ambivalence of Control in Experimental Investigation of Historically Contingent Processes
Bibliographic record
Abstract
Abstract Historical contingency is commonly associated with unpredictability and outcome variability. As such, it can be seen as an undesirable aspect of experimental investigations. Many might agree that experimental methodologies that include enough control help to by-pass this problem and thereby make for more secure knowledge. Against this received view, we argue that, for at least some historically contingent processes, an over-emphasis on control might mislead by obscuring the very object of investigation or by preventing fruitful discoveries. In discussing cases from evolutionary biology, developmental biology, and geochemistry/astrophysics, we show how investigating through approaches that don’t prioritize environmental control, while allowing for greater variability of outcomes, better respects the object/environment entanglement of these systems. Finally, we defend the idea that, despite the lower level of control, these types of experiments do not have a lower epistemic value than more highly controlled experiments.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".