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Record W1965489187 · doi:10.1080/14649365.2010.521852

Memorialisation and remembrance: on strategic forgetting and the metamorphosis of psychiatric asylums into sites for tertiary educational provision

2010· article· en· W1965489187 on OpenAlexaffabout
Robin Kearns, Alun E. Joseph, Graham Moon

Bibliographic record

VenueSocial & Cultural Geography · 2010
Typearticle
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDemiseNarrativeContext (archaeology)Successor cardinalStigma (botany)Closure (psychology)SociologyHistoryAestheticsPolitical sciencePsychologyPsychiatryLawArchaeologyArtLiterature

Abstract

fetched live from OpenAlex

This paper builds on earlier investigations of psychiatric asylum closure by focusing on their not infrequent successor role as educational facilities. We ask two questions: what conditions underpin a transition to educational re-use, and how is former asylum use remembered and memorialised in the successor context? Through recounting and interpreting the histories of acquisition and adaptation at two sites (Carrington, Auckland and Lakeshore, Toronto), we build a narrative that suggests a variable response to the shadows cast by stigma and the vilification of asylum. We distinguish between memorialisation (material reminders on site) and remembrance (narratives of past use). Former asylum sites, we contend, are attractive for educational users for their campus-like settings, range of buildings and (now) suburban locations. For city residents and planners replacing one institutional use with another keeps the site green, brings employment, and retains semi-public access. Memorialisation is often strategically low-key and remembrance more personal and individual. The net result is a relict landscape that speaks to the transcendence of stigma despite the relatively recent demise of the asylum.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.021
GPT teacher head0.345
Teacher spread0.323 · 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 designTheoretical or conceptual
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

Citations33
Published2010
Admission routes2
Has abstractyes

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