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Record W4200007134 · doi:10.1007/978-981-16-6253-9_5

Nostalgia

2021· book-chapter· en· W4200007134 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsnot available
FundersBC HydroCalifornia Public Utilities CommissionJohns Hopkins UniversityU.S. Department of State
KeywordsEnergy (signal processing)AestheticsSociologyPolitical scienceArt

Abstract

fetched live from OpenAlex

Abstract Nostalgia is a longing for the past and the way we remember how things used to be done, including a wish for things to stay the same. Nostalgia is a central part of understanding societal responses to change because every new technology and way of doing something is, in effect, competing with nostalgia. In this chapter, I examine how nostalgia can hamper efforts at energy innovation, particularly in terms of how it blinds us to change already under way, and how memories of certain innovations can in subtle ways encourage or hinder innovation. I explore three diverse case studies about nostalgia: memories of pioneering international smart grid experiments, scarce data about off-grid households, and big infrastructure energy solutions.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.902
Threshold uncertainty score1.000

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 integrity0.0010.000
Insufficient payload (model declined to judge)0.3440.020

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 head0.039
GPT teacher head0.306
Teacher spread0.267 · 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

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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