Vancouver Statement on Collections as Data (ES)
Bibliographic record
Abstract
English translation French translation Arabic translation Desde que se publicó la declaración Santa Barbara Statement on Collections-as-data (2017) se ha producido un crecimiento en la participación en colecciones usadas como fuentes de datos internacionalmente. Las instituciones, tanto grandes como pequeñas, o de forma individual o colectiva, han invertido en el desarrollo, la facilitación de acceso y el respaldo del uso computacional responsable de colecciones usadas como fuentes de datos. Por ello, es necesaria una declaración actualizada a la luz de una mayor implementación comunitaria de colecciones usadas como fuentes de datos en el contexto de un entorno de datos cada vez más complejo. La declaración Vancouver Statement sugiere un grupo de principios para cavilar las cuestiones que el trabajo de Collections-as-data genera, como parte de un esfuerzo de expansión global, interprofesional e interdisciplinar para empoderar la memoria, el conocimiento y a los responsables de la gobernanza de los datos (facultativos y académicos), cuyo objetivo es dar soporte al desarrollo responsable y el uso computacional de colecciones usadas como fuentes de datos. Este rol de responsabilidad de gobernanza solo crece en importancia a medida que las aplicaciones de inteligencia artificial, entrenadas con enormes cantidades de datos, (incluidas las colecciones usadas como fuentes de datos) impactan en nuestras vidas cada vez de forma más penetrante. The Vancouver Statement is the product of diverse contributions from the participants of the working event, Collections as Data: State of the Field and Future Directions, held April 25-26, 2023 at Internet Archive Canada in addition to asynchronous community feedback. Professional translation of the Vancouver Statement was provided by Transolution. Special thanks go to Gimena del Rio Riande and Gaëlle Béquet for additional review of statement translations.
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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 | 0.030 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.031 | 0.012 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.192 | 0.238 |
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".