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Record W2593707339

Having the Time of Our Lives? How Threat Appraisal is Influenced by the Subjective Nature of Time

2015· dissertation· en· W2593707339 on OpenAlexfundno aff
Rachelle Sass

Bibliographic record

VenueYorkSpace (York University) · 2015
Typedissertation
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsnot available
FundersYork University
KeywordsPsychologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Temporal construal is the cognitive process that determines an event’s location in time and the experience of its distance from the present. The greater the temporal distance, the more likely events are represented in abstract versus concrete features. This experiment examined temporal construal’s effect on threat appraisal of a stressful medical procedure, where the manipulation involved university students imagining the procedure in concrete or abstract terms. The near-future group was expected to interpret the procedure as nearer and more threatening than the distant-future group on questionnaires. An Implicit Association Test (IAT) measured response latencies during categorization of stimuli into paired concepts (threat and time). A significant interaction was found between a personality trait and temporal construal on the perceived distance of the procedure, t(189) = 2.14, p = .03. IAT results found that participants were faster at categorizing stimuli into congruent versus incongruent pairs, t(179) = 4.05, p < .001.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.285
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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