What Is ‘Good’ Science? How Disciplinary Norms and Expectations Discourage Broad Interdisciplinary Collaboration
Bibliographic record
Abstract
Abstract Notions of ‘good’ science exert a powerful influence over scientists’ decisions about how research should be conducted and rewarded. Rarely are broad interdisciplinary collaborations, such as those between scientists and philosophers of science, characterized as ‘good’ science, despite philosophy’s relevance to scientific inquiry. We draw on Bourdieu’s concepts of field and habitus to explore how notions of ‘good’ science generate systemic barriers to scientists’ ability to collaborate with philosophers of science. We conducted semi-structured interviews with scientists and engineers who have engaged in research collaborations with philosophers of science and then used thematic codebook analysis to examine participant attitudes, disciplinary expectations, and academic incentive structures. We identify two different conceptions of ‘good’ science: field-aligned science, which is a more technical, data-driven approach that conforms to disciplinary incentive structures, and field-disruptive science, which asks more foundational questions but that tends not to be rewarded within scientific disciplines. Given how philosophy can enhance science, we argue that scientific communities would benefit from actively valuing science undertaken in collaboration with philosophers, but that doing so would require a shift in the field and the habitus that it encourages. Such a shift would also make science more conducive to other types of broad interdisciplinary collaboration.
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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 | 0.109 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".