Bibliographic record
Abstract
This update is my first in two years, having foregone my annual update in the 2022 volume to give as much space as possible to our authors and reviewers. The year 2022 began with a special issue, “The Neuroscience of Film,” guest edited by Vittorio Gallese and Michele Guerra, followed by two issues comprising original research articles and book reviews by authors based in Australia, Canada, China, Denmark, Finland, the Netherlands, Russia, and the United States. I am heartened by both the research and the geographical inclusivity of our journal and our society. I'm grateful to all three of our associate editors for their efforts, and I wish to offer special thanks to Aaron Taylor for his work as book review editor—a job he has taken up with a particular focus on outreach to colleagues who share the interests of the journal and society but have not yet attended a conference, become a member, or submitted a manuscript. Building connections within and across disciplines is crucial to the continued success of SCSMI and Projections, so please: do what you can to spread the word by circulating calls, renewing your institution's subscription, and the like.
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.232 | 0.186 |
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".