Bibliographic record
Abstract
A nefarious plot of forgery and fame that shocked the world in 1996.[1] A hidden tunnel of dubious origin that baffled Kingston’s historical experts in 2009.[2] But how could the world of British counterfeiting possibly collide with Kingston’s local mysteries? For Queen’s annual I@Q Conference, I would love to present a research poster answering just that. The key to both cases lies in the importance of archival evidence to uncover misinformation both large and small. In the modern day, historians are constantly surrounded with conflicting arguments and many have relied on archival evidence to settle debates and quell misinformation. But are archives always reliable? Can archives lie? To best address these questions, my poster would engage with both the theoretical aspect of archival diplomacy and the practical side. It would expand upon the key examples mentioned previously and analyse the historical use and abuse of archives in the name of stopping misinformation. Therefore, such a poster would be significant because it would explore the ambivalent position of the archival record and expose the dangers of archival manipulation at an international level. A tale of ingenuity, the case of the Staffordshire art forgeries revolved around the infiltration of leading archives and planting of false documents to bolster the legitimacy of counterfeit paintings.[3] The uncovering of such a plot shocked artists and archivists alike and suddenly cast doubt on the reliability of the archival record. On a much smaller scale, a similar archival debate was battled out in Kingston with intriguing results. [1]Rodney G.S Carter, “Tainted Archives: Art, Archives, and Authenticity,” Archivaria 63, no.1 (2007), 75-86. [2]Rodney Carter, email message to author, February 14, 2023. [3] Carter, “Tainted Archives,” 79-80.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.028 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".