Marshland revival: a narrative rephotography essay on the False Creek Flats neighbourhood in Vancouver
Bibliographic record
Abstract
This article proposes to expand current thinking about rephotography and how it might focus on a specific geographic terrain, thereby validating research conducted from 2018 to 2020 in Vancouver, Canada. The research site is located in the False Creek Flats post-industrial district, a district currently undergoing urban regeneration while pursuing a major restructuring programme (to be completed in 2037). The programme is being undertaken by the City of Vancouver. This paper attempts to identify the transformational dynamics that characterise the urban area by drawing primarily on two visual sources: photographs taken in situ and research conducted in Vancouver’s visual archives. After taking composition into account, these sources will play a key role in mounting a proposal that highlights narrative rephotography. The montages produced will then make it feasible to construct a narrative that runs counter to the official version: a city which has no past. The montages strive both to reintroduce a temporal arc that will subsume the almost-forgotten millennial history of the area and to depict one of its likely futures. Indeed, in contrast to the promising future set forth in the urban regeneration plan, another uchronia is, paradoxically, reconnecting with its aquatic past, as global heating accelerates rising water levels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".