Bibliographic record
Abstract
Archives Unlocked, the U.K. National Archives’ strategic vision for the archive sector, identifies the need for diversity to be embedded in all parts of the archives sector. As workers, we need to ensure that “the rich diversity of society is reflected in our archives’ collections, users and workers” (The National Archives, 2017, p.13). Despite strategic aims and investment in specific schemes (delivered by The National Archives, Creative Skillset, and the Heritage Lottery Fund) which seek to diversify the sector, there are still structural barriers which prevent the workforce from diversifying and realising these ambitions. In 2017, the authors of this paper began collaborating on a grassroots project to explore the experiences of archive workers from marginalised backgrounds. The project collected anonymous survey data from 97 people which explored experiences of work and qualification. As two archive workers who have experience of accessing the archive sector workforce via diversity bursaries and scholarship, we wanted our research to articulate a common set of frustrations that are often shared but rarely documented or consulted when developing diversity and inclusion strategies and schemes. By utilising lived experiences as our main research data in this paper, we re-centre discussions about diversity and inclusion around the lived experience of those currently on the margins of the archive workforce.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.472 | 0.221 |
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".