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Record W2018769346 · doi:10.1177/0146167213501559

Marking Time

2013· article· en· W2018769346 on OpenAlexaff
Johanna Peetz, Anne E. Wilson

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsWilfrid Laurier UniversityCarleton University
Fundersnot available
KeywordsMindsetPsychologySocial psychologyPerceptionSelfCognitive psychologySelf-enhancementComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Temporal landmarks such as birthdays and significant calendar dates structure our perception of time. People might highlight temporal landmarks spontaneously in an effort to regulate connections between temporal selves. Five studies demonstrated that landmarks are used spontaneously to induce psychological separation from undesirable temporal selves. Participants were more likely to think of events that fell in between the current and the future self if an imagined future self was negative than if it was positive (Studies 1a, 1b, and 2). Furthermore, when a self-enhancement mindset was activated, participants were more likely to call to mind intervening temporal landmarks to protect themselves from a negative future self than when this mindset was not activated (Study 3). Finally, when psychological separations between the current self and a negative future self were introduced through alternate means, participants no longer selectively used landmarks to separate themselves from this future self (Study 4).

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.005

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 head0.063
GPT teacher head0.382
Teacher spread0.318 · 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

Citations62
Published2013
Admission routes1
Has abstractyes

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