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Record W4400486499 · doi:10.1080/13527258.2024.2378488

After the light: the reuse and replica of Canada’s historic lighthouses

2024· article· en· W4400486499 on OpenAlexafffundabout
Nicholas Lynch, Bryhanna Greenough

Bibliographic record

VenueInternational Journal of Heritage Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsMemorial University of Newfoundland
FundersCanada First Research Excellence FundOcean Frontier Institute
KeywordsAdaptive reuseReuseCultural heritageReplicaScrutinyIdentity (music)PoliticsSociologyHistoryEnvironmental ethicsArchaeologyPolitical scienceLawAestheticsEngineeringArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.262
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes3
Has abstractyes

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