The Organization of Science: A Multi-Disciplinary Approach
Bibliographic record
Abstract
The organization of scientific work has been of considerable interest to a diverse body of academicians, ranging from economists to sociologists to historians. Moreover, a focus on science and the production of knowledge has spurred the development of topics ranging from status differentials to regional spillovers to technological trajectories. The goal of this symposium is to bring together a multi-disciplinary coterie of scholars to present new research at different points in the knowledge production value chain. Ranging from drug discovery to scientific careers, we hope to engage one another across disciplines, topics, and the presenter- audience divide. Me Too or Something New: Credit Constraints and Drug Similarity Presenter: Danielle Li; Harvard Business School Where is the Promised Land? Presenter: Waverly W. Ding; U. of Maryland Is Bigger Better? Lab Productivity and Lab Size Presenter: Christopher C. Liu; U. of Toronto The Impact of Open Access Mandates on Invention Presenter: Kevin Bryan; U. of Toronto Helpful Thirds and the Durability of Collaborative Ties Presenter: Alexander Oettl; Georgia Institute of Technology
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.026 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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, 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".