The Annotated Archive of Archival Resources
Bibliographic record
Abstract
The Annotated Archive of Archival Resources is a new educational resource that is produced and managed by A/CA members Katie Russell and Sam Thomson at Concordia University. This project takes the form of a shared Zotero bibliography with annotated entries submitted from researchers across the Archive/Counter-Archive research network. The aim of this project is to provide an accessible, public-facing resource to facilitate researchers in finding scholarship relevant to the study and creation of audiovisual archives by Indigenous Peoples (First Nations, Métis, Inuit), Black communities and People of Colour, women, LGBT2Q+ and immigrant communities. It offers a categorized and annotated listing of important works from diverse bodies of scholarship related to this central topic. The Annotated Archive of Archival Resources is integrated with Zotero to make use of the platform's searchability and capacity for sharing and formatting bibliographic data. With Zotero's tag system, researchers using the bibliography are able to filter scholarly resources by a wide variety of topics and easily locate annotations that address their specific fields of inquiry. This record includes a ris file which can be imported into bibliographic software like Zotero.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.022 | 0.037 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.052 |
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".