Why the Realism Debate Matters for Science Policy: The Case of the Human Brain Project
Bibliographic record
Abstract
There has been a great deal of skepticism towards the value of the realism/anti-realism debate. More specifically, many have argued that plausible formulations of realism and anti-realism do not differ substantially in any way (Fine 1986; Stein 1989; Blackburn 2002). In this paper, I argue against this trend by demonstrating how a hypothetical resolution of the debate, through deeper engagement with the historical record, has important implications for our criterion of theory pursuit and science policy. I do this by revisiting Arthur Fine’s ‘small handful’ argument for realism and show how the debate centers on whether continuity (either ontological or structural) should be an indicator for the future fruitfulness of a theory. I then demonstrate how these debates work in practice by considering the case of the Human Brain Project. I close by considering some potential practical considerations of formulating meta-inductions. By doing this, I contribute three insights to the current debate: 1) demonstrate how the realism/anti-realism debate is a substantive debate, 2) connect debates about realism/anti-realism to debates about theory choice and pursuit, and 3) show the practical significance of meta-inductions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".