Digital decolonization and activist tagging in the Post-Apology Residential School Database
Bibliographic record
Abstract
The Post-apology Residential School Database, or PARSD, is a collection of digital and digitized news media responses to and representations of Indian Residential Schools since the Canadian government’s official apology in Parliament on June 11th, 2008. In this conceptual paper, we discuss PARSD tagging practices, describing how our archival description approach is informed by feminist and anti-colonial theoretical frameworks and outlining how project members and ‘guest taggers’ describe, organize, and display PARSD records to promote decolonization. We conclude by considering both the potential and possible limitations that these practices may play in decolonizing and reconciling research.La Base de données sur les pensionnats après la présentation des excuses est une collection de réactions et de représentations des pensionnats indiens dans les médias numériques et numérisés depuis les excuses officielles du gouvernement canadien au Parlement le 11 juin 2008. Dans cet article conceptuel, nous discutons des pratiques de marquage dans la base de données, en décrivant comment notre approche de description archivistique est influencée par les cadres théoriques féministes et anticoloniaux et comment les membres du projet et les 'tagueurs invités' décrivent, organisent et affichent les notices de la base de données de façon à promouvoir la décolonisation. Nous concluons en considérant à la fois les limites potentielles et possibles que ces pratiques peuvent imposer dans la décolonisation et la réconciliation de la recherche.
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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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".