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Record W2809425745 · doi:10.3138/gsi.12.1.06

Questions of Privacy and Confidentiality after Atrocity: Collecting and Retaining Records of the Residential School System in Canada

2018· article· en· W2809425745 on OpenAlexaffvenueabout
Tricia Logan

Bibliographic record

VenueGenocide Studies International · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsConfidentialityInternet privacyGenocideEconomic JusticeInformation privacyBusinessData collectionPolitical scienceComputer securityCriminologyPsychologyLawSociologyComputer science

Abstract

fetched live from OpenAlex

Record collection and record preservation have direct consequences for survivors and often determine the efficacy of the production of genocide memory. Currently, there are increased restrictions that limit our access to evidence and threaten long-term preservation of records. This article will focus on the recent decision to permanently destroy Independent Assessment Process files within fifteen years. These records contain personal disclosures of serious abuses and their destruction reflects an uneasy precedent with record conservation. While institutions strive to protect the privacy and safety of survivors and respect the use of their information, there are concerns that the state, churches and organizations obscure memory through the use or misuses of records or use the terms of privacy to protect themselves. This paper will draw on examples of conflicts within access, privacy, respect, and memory that inevitably contribute or detract from efforts in reconciliation or transitional justice.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.057
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.009
Science and technology studies0.0540.023
Scholarly communication0.0110.003
Open science0.0040.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.246
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2018
Admission routes3
Has abstractyes

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