Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it