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Record W1997148281 · doi:10.1016/j.jarmac.2014.07.006

Breakdown in the metacognitive chain: Good intentions aren’t enough in high school.

2014· article· en· W1997148281 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

VenueJournal of Applied Research in Memory and Cognition · 2014
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
FundersCanadian Association for the Study of the Liver
KeywordsPsychologyMetacognitionTest (biology)Social psychologyControl (management)Mathematics educationCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

a b s t r a c t Two experiments examined the effects of a metacognitive betting implementation in high school Biology students. The results showed that people were generally good at monitoring their own knowledge in that students performed better on items judged with high bets than items judged with low bets. We also found that those who were required to make bets, as compared to those who did not, had higher intentions of studying for longer periods of time, prior to the test. However, there were no differences in actual study time. Nor was there a difference in final performance, as one would expect, between the betters and the non-betters. In summary, we found indication of (1) good intentions when using the betting procedure, but (2) breakdown in the metacognitive chain during control. That is, while requiring students to make deliberate judgments improves their intentions to study, they, unfortunately, fail to carry out those intentions.

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)0.007
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.072
GPT teacher head0.394
Teacher spread0.322 · 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