MétaCan
Menu
Back to cohort

Skimming the Surface

2004· article· en· W2141365577 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 · 2004
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyTest (biology)Think aloud protocolRead aloudCognitive psychologySocial psychologyReading aloudLinguisticsReading (process)Computer science

Abstract

fetched live from OpenAlex

It has become almost a maxim that "talking through" a problem is advantageous. Contrary to this wisdom, studies from numerous domains have demonstrated that describing one's thought processes or analyzing a judgment may, in some circumstances, actually impair performance. The two experiments reported here built upon prior work by examining the effect of verbalization on the retrieval of analogies. Participants read a series of 16 short stories. Later, they were presented with 8 test stories and indicated whether these stories were analogies of the stories they had read previously. Each test story shared the same deep structure with one prior story and only surface characteristics with another prior story. Half of the participants completed the test while thinking aloud, and half did not think aloud. In both experiments, participants who thought aloud were more likely to retrieve surface matches and less likely to retrieve true analogies than participants who did not verbalize their thoughts during the test.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.067
GPT teacher head0.439
Teacher spread0.372 · 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