A representação arquivística na tradição canadense: subsídios para elaboração de um modelo ideal de ensino por meio da semântica textual
Bibliographic record
Abstract
It presents a study relating Linguistics and Archival Science through semantics and Archival Representation. The general objective was to contribute to the construction of a theoretical and methodological reference regarding the Archival Representation in the Canadian context, aiming the creation of a teaching model based on textual semantics, and its specific objectives were to analyze research instruments of two Canadian institutions; to observe the linguistic analysis criteria established for this research, in these instruments; compare the approaches in representation in the two selected institutions. The method used to achieve these objectives is characterized by being an exploratory, theoretical and documentary study, having the semantic and textual construction criteria of meaning and constituent elements of textual coherence and cohesion as a methodology of analysis for understanding the paths of Archival Representation in the research tools of the Canadian Archives - Library and Archives Canada (LAC) and Provincial Archives of Manitoba. We delimitated these two Canadian institutions from federal and state action spheres, respectively, to analyze the classification and description of archives practices due to the importance to the scenario of the chosen country, since these institutions present different approaches in representation.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".