KATHERINE BIBER and TRISH LUKER, eds., Evidence and the Archive: Ethics, Aesthetics and Emotion
Bibliographic record
Abstract
As Katherine Biber and Trish Luker rather flatly understate in their introduction to this generally stimulating collection of essays, "the notion of the 'archive' has been claimed and contested within cultural and critical discourse in the humanities" (p. 6).The proof of the editors' contention is to be found in the pages of their book.In Evidence and the Archive, we discover, for example, the Solomon Islands National Archives, the type of state repository of official records familiar to most archivists and historians.But we also run up against "law's archive as 'commandment'" and "as genre" (p.124).I am no longer certain what is to be gained in the long-running tug-of-war over the archive as workaday, bureaucratic institution or Derridean metaphor.The editors would seem to agree, concurring with one cultural theorist that "to some extent, the term has to be surrendered" (p. 6).I, for one, surrender.Thus freed up, one is better able to appreciate the many useful ways this collection expands the notion of law's archive and the afterlife of legal evidence.As a historian who for over 25 years has been researching court records to write queer history, I expected to encounter a series of essays on the by-now familiar methodological possibilities and limitations of using the kinds of evidence -textual, photographic, artifactual -that one finds in law's archive.Readers of this journal might expect to be treated to discussions of the acquisition and processing of court records, along with the rules governing access to them.None of these matters is entirely absent.However, the book is aimed at legal scholars (it originally appeared in 2014 as an issue of the Australian Feminist Law Journal and, incidentally, the book reproduces what was then the journal's sloppy footnoting format), who, it is claimed, have not sufficiently grappled with the "archival turn."It's a paradoxical state of affairs in view of the law's voluminous contributions to archives, both public and private.Yet this is no simple primer on archives for those in the legal professional archivists and community members.As mentioned above, Indigenous perspectives and perspectives from non-Western countries would enrich the discussion and provide an outlet for voices that are often silent or silenced.Pairing theoretical essays with case studies that focus on concrete applications of the same topic would also strengthen the continuing dialogue between theory and practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.017 |
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".