Bibliographic record
Abstract
Endnotes Katherine Malone, VPR Editor and Fionnuala Dillane, RSVP President VPR Fall/Winter 2022 Over the past two years, RSVP has worked to sustain a vibrant scholarly community and add value to our membership despite the challenges of pandemic lockdowns, travel bans, and changes to academic workloads. Though we were unable to gather in person for our annual conference, we held two successful virtual conferences in 2021 and 2022 and organized and hosted a varied series of online seminars, workshops, and symposia (see our YouTube channel for recordings of some of these events: https://www.youtube.com/@rs4vp). Victorian Periodicals Review has also responded to these challenging times by extending deadlines and adjusting our schedule to support authors, reviewers, and editorial staff affected by the pandemic. To bring the journal back to its quarterly publication schedule, our next issue will combine fall and winter 2022. As always, we are proud to nurture and publish the highest quality scholarship in the field of periodical studies. As we look ahead to 2023, we hope you will join us in person in Caen, France, for our annual conference in July, as well as onscreen at our virtual events and in the pages of VPR’s fifty-sixth volume. [End Page 313] Copyright © 2023 The Research Society for Victorian Periodicals
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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 | 0.004 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.386 | 0.265 |
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".