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Record W1948909166 · doi:10.1177/0956797615591771

Concreteness and Psychological Distance in Natural Language Use

2015· article· en· W1948909166 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2015
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsMcMaster University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsConcretenessPsychologyCognitive psychologyRepresentation (politics)LinguisticsMental representationPsycholinguisticsFunction (biology)Social psychologyCognition

Abstract

fetched live from OpenAlex

Existing evidence shows that more abstract mental representations are formed and more abstract language is used to characterize phenomena that are more distant from the self. Yet the precise form of the functional relationship between distance and linguistic abstractness is unknown. In four studies, we tested whether more abstract language is used in textual references to more geographically distant cities (Study 1), time points further into the past or future (Study 2), references to more socially distant people (Study 3), and references to a specific topic (Study 4). Using millions of linguistic productions from thousands of social-media users, we determined that linguistic concreteness is a curvilinear function of the logarithm of distance, and we discuss psychological underpinnings of the mathematical properties of this relationship. We also demonstrated that gradient curvilinear effects of geographic and temporal distance on concreteness are nearly identical, which suggests uniformity in representation of abstractness along multiple dimensions.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.081
GPT teacher head0.396
Teacher spread0.315 · 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