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Record W4393971353 · doi:10.7202/1104265ar

Transferred, Preserved, and Destroyed

2023· article· en· W4393971353 on OpenAlexvenueaboutno aff
Ryan Eyford

Bibliographic record

VenueArchivaria · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsArtHistoryAncient history

Abstract

fetched live from OpenAlex

During the 1950s, the Province of Manitoba microfilmed and then destroyed thousands of files created by the federal Department of the Interior’s Dominion Lands Branch (DLB). These records, dating from about 1870 to 1930, were transferred from the federal government to the province in the immediate post-war period. They were drawn from a group of more than 5.6 million files occupying 11,640 square feet of office space in downtown Ottawa. During the Second World War, the civil servants responsible for the DLB files were pressured by their superiors to destroy the files in order to free up space and filing cabinets. DLB officials, although not trained archivists, took their responsibility as custodians of the records seriously and sought to prevent the wholesale and indiscriminate destruction of the files. They were supported by archivists who considered the DLB files to be valuable historical documents on the colonization of Western Canada. Eventually, the conflict between preservation and destruction was resolved by dispersing the records; some were transferred to the western provinces and territories, while the remaining files were deposited in the Public Archives of Canada. The first files to be transferred were those related to lands in Manitoba. This article clarifies the provenance of the DLB’s Manitoba files and argues for their enduring value as records of the history of settler colonialism in the province while also revealing the role of non-archivist civil servants as custodians of government records in the mid- 20th century.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.385

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.211
Teacher spread0.166 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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