How Big is the Tent? Toward a Stronger Identity for Organizational Neuroscience and Biology
Bibliographic record
Abstract
The fields of organizational neuroscience and biology are still evolving, presenting an opportune moment to cultivate a more defined and robust identity for these emerging fields. Accordingly, it is important to consider the demarcation between organizational neuroscience and biology research, versus research disciplines that may be relevant, but nevertheless more tangential or auxiliary. This delineation is crucial for clarifying the disciplinary boundaries and asserting the foundational concepts, theory, and research methods of organizational neuroscience and biology. Our goal is not to demean fields of study or research methods that are distinct from, albeit potentially relevant to, organizational neuroscience and biology. Rather, we simply seek to provide more clarity when the terms ‘organizational neuroscience’ or ‘organizational biology’ are used. As described in detail below, our panel symposium aims to delve into several pivotal issues that can help shape the identity of these fields as they move forward. Those issues include (1) methods and concepts, (2) the potential for excessive reductionism, and (3) the “Big N/B” vs. “little o” dilemma in organizational neuroscience and biology.
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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 | 0.110 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.073 |
| Scholarly communication | 0.037 | 0.047 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.018 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 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".