United Church of Canada's Reconciliation Documents and the Indexing of Collective Memory
Bibliographic record
Abstract
Following a distinct trajectory in the field of Knowledge Organization, I explore how indexes are part of the structure of our everyday lives. Drawing on extensive archival research, I look at documents created and used by the United Church of Canada as part of its reconciliation work with Indigenous peoples. I conclude that these documents index the narrative the church tells about itself—and therefore its identity—as part of the development and maintenance of the UCC’s evolving collective memory. My findings reinforce Knowledge Organization’s new line of inquiry while also complicating its message concerning the nature of infrastructure. En suivant une trajectoire unique issue du domaine de l'organisation des connaissances, j'explore comment les index font partie de la structure de notre vie quotidienne. En m'appuyant sur des recherches archivistiques approfondies, j'examine les documents créés et utilisés par l'Église Unie du Canada dans le cadre de son travail de réconciliation avec les peuples autochtones. Je conclus que ces documents indexent le récit que l’Église raconte sur elle-même - et donc sur son identité - dans le cadre du développement et du maintien de la mémoire collective en évolution de l’ÉUC. Mes conclusions renforcent cette nouvelle avenue de recherche du domande de l'organisation des connaissances, tout en complexifiant son message par rapport la nature de l’infrastructure.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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.
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".