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Record W609416242

Ecologies of Affect: Placing Nostalgia, Desire, and Hope

2013· book· en· W609416242 on OpenAlexaff
Tonya Davidson, OndinePark, RobShields

Bibliographic record

Venuenot available
Typebook
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsAffect (linguistics)AestheticsSpace (punctuation)Value (mathematics)Identity (music)The ImaginaryPower (physics)Tone (literature)SociologyRelation (database)Character (mathematics)EpistemologyMedia studiesArtPsychologyLiteraturePsychoanalysisComputer scienceCommunication
DOInot available

Abstract

fetched live from OpenAlex

Ecologies of Affect offers a synthetic introduction to the felt dynamics of cities and the character of places. The contributors capture the significance of affects including desire, nostalgia, memory, and hope in forming the identity and tone of places. The critical intervention this collection of essays makes is an active, consistent engagement with the virtualities that produce and refract our idealized attachments to place. Contributors show how place images, and attempts to build communities, are, rather than abstractions, fundamentally tied to and revolve around such intangibles. We understand nostalgia, desire, and hope as virtual; that is, even though they are not material, they are nevertheless real and must be accounted for. In this book, the authors take up affect, emotion, and emplacement and consider them in relation to one another and how they work to produce and are produced by certain temporal and spatial dimensions. The aim of the book is to inspire readers to consider space and place beyond their material properties and attend to the imaginary places and ideals that underpin and produce material places and social spaces. This collection will be useful to practitioners and students seeking to understand the power of affect and the importance of virtualities within contemporary societies, where intangible goods have taken on an increasing value.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.258
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.307
Teacher spread0.276 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations18
Published2013
Admission routes1
Has abstractyes

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