Bibliographic record
Abstract
We are happy to present the Fall 2017 issue of Constellations journal. Included within are four diverse undergraduate papers ranging vastly in topic. We made a conscious effort to encourage submissions from a wide range of disciplines, provided the work could be broadly considered “historical” in scope. Interdisciplinary cooperation is something that we feel should be celebrated and promoted, and we are currently working with other student journals and organizations to bring an Arts-wide undergraduate research conference to life this spring.We hope that you enjoy the variety of topics covered in this edition, and appreciate your interest and support in Constellations. We would especially like to thank our review team, without which Constellations would remain starcrossed... Assistant Editors:Lucas Nowosaid and Juliana McPhail Senior Reviewers:Bronte WellsSamantha KallenShelby CollingLiuba Gonzalez De ArmasYunus SahinAlex HoggAnastasia Pavlic General Reviewers:Emily HainesFarah KhalidMiranda RondeauStacy FairfulCassidy MunhollandDana KanervaLexi BrunnerDevonne BrandysKatie DuHeather Mark
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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.380 | 0.331 |
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".