MétaCan
Menu
Back to cohort

Appraisal and Original Order

2023· book-chapter· en· W4389922897 on OpenAlex

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArchives · 2023
Typebook-chapter
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsArchivistOrder (exchange)BureaucracyProcess (computing)Power (physics)Computer sciencePolitical scienceLibrary scienceLawBusiness

Abstract

fetched live from OpenAlex

Abstract This chapter introduces two fundamental components of archival practice: appraisal and original order. The most important power exercised by the archivist is deciding whether records are kept or destroyed, a process known as appraisal. Only a small proportion of records survive the appraisal process. The implications of appraisal and destruction are illustrated with reference to the Windrush scandal in the United Kingdom. In order to preserve information about the bureaucracy which created the archive, it is essential to preserve the original order file and other series. However, original order means that interpretations are inextricably tied into the corporate culture of the body which created the archive. Digital records challenge traditional approaches to appraisal and original order, and raise the possibility that we can transcend the power structures associated with the conventional archive.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.541
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.038
GPT teacher head0.224
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it