After the light: the reuse and replica of Canada’s historic lighthouses
Bibliographic record
Abstract
Lighthouses are enduring symbols of maritime heritage, representing the history and identity of coastal communities. Over the last few decades, however, these structures have been increasingly decommissioned and divested especially in response to the rise of navigational technologies and costly maintenance. Remarkably little academic scrutiny has been paid to the present and future challenges of these sites, and even less attention has been given to contexts of lighthouse closure, reuse, and replication. In response, this paper combines lighthouse infrastructure data with media analyses and qualitative interviews to explore the ‘afterlives’ of Canada’s historic lighthouses, examining their transformations and the cultural politics involved in their reuse and replica (or faux lighthouses). We argue that historic lighthouses are not only significant features of maritime heritage but also serve as contemporary sites of coastal change and growth. By studying their afterlives, the paper sheds light on the intricate and complicated lifecycles of lighthouses and the communities that depend on them. Overall, we contribute to a broader understanding of coastal heritage, emphasizing the importance of examining adaptive reuse and the emergence of replica lighthouses as practices that renegotiate established values, meanings, and emotions associated with these iconic structures.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".