Bibliographic record
Abstract
This chapter analyzes the history of the Land Bridge at Fort Vancouver and how Confluence Project&s;s commission, planning, and construction evolved from its inception to its completion in 2008. Early criticisms of Confluence faulted Lin for reproducing Eurocentric historical narratives through the deployment of traditional iconographical elements, including landscape paintings and photographs of the site&s;s industrial facilities incorporated into the partition wall facing the Columbia River. To some, these features overrepresented a post-settler colonial world order at the expense of tribal histories that have characterized the site for a much longer period of time. ( Daehnke 2018 , 511) But a broader, more detailed reading of the many art elements and the unique design of the Bridge itself complicate this interpretation. Lin, Jones, and the Confluence Project team collaborated to foreground Native experience by reconnecting the Klickitat Trail to the Columbia River, evoke habitat loss and recovery through the restoration of native species and water conservation design features, and recontextualize Fort Vancouver&s;s pivotal role in the development of the Pacific Northwest within a much longer Indigenous history of place. The Bridge purposefully juxtaposes millennia of Native American sustainable environmental practices with an industrialized riverscape less than two centuries old.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.126 | 0.028 |
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".