Bibliographic record
Abstract
Abstract: Historians and archivists approach the documentary past differently, as they consider, respectively, the ‘archive’ (singular) and ‘archives’ (plural). The former focuses on issues of power, memory, and identity centred upon the initial inscription of a document (or series of documents). The latter concentrates on the subsequent history of documents over time, including the many interventions by archivists (and others) that transform (and change) that original archive into archives. Despite making good common cause in lobbying over public policy, and initially sharing values based on objective, scientific history, the two professions have drifted apart in recent decades. This essay explores the reasons for this divergence by analyzing the history of the two professions and highlighting resulting misconceptions that blind both to deeper nuances of the multiple contexts surrounding records that may enhance their understanding and use. It concludes that archives are not unproblematic storehouses of records awaiting the historian, but active sites of agency and power. Until recently, it has been in the interests of both professions to deny (or at least not interrogate) the subjectivity of archives. Both professions could benefit significantly, therefore, from a renewed partnership centred upon the history of the record to produce better history.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.819 | 0.547 |
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".