Bibliographic record
Abstract
The launch of a journal is no small task, and this monumental effort was the work of many. We are very grateful for the support and assistance of the journal’s editorial board and international advisory board; the members of the Persona cluster of the Persona Celebrity Publics (PCP) Research Group; the national and international colleagues involved in the peer-review process, and the participants of the Persona Studies Working Papers Syposium and Exhibition hosted at Deakin University in February 2015. It would also be amiss of us if we did not flag the particular contributions of Pat Scott and Josipa Crnic from the Deakin University Library, who worked closely with the editors to set up the online systems. Their expertise in OJS, and their unflagging support and enthusiasm has been of tremendous value. We are grateful for the intellectual and financial support offered by PCP and thank the leadership team—Sean Redmond, Toija Cinque, Glenn D’Cruz, and Kristen Demitrious—for advocating on the project’s behalf. Glenn D’Cruz has been the linchpin to the creative portion of this issue: his efforts in curating the exhibition and co-ordinating the reproduction of that work here are very much appreciated. The business and busyness of hosting the Symposium, setting up and managing this open-access journal, and editing this issue, has been, for us, deeply satisfying. Thank you, all, for your help in making it so. We look forward to working with you again on the next issue.
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 | 0.007 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.402 | 0.416 |
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".