Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it