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Record W2058004936 · doi:10.1080/00221309.2010.540592

The Role of Syllables in Anagram Solution: A Rasch Analysis

2011· article· en· W2058004936 on OpenAlex
John W. Adams, Mark H. Stone, Robert D. Vincent, Steven Muncer

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

VenueThe Journal of General Psychology · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnagramsRasch modelAnagramSyllableBigramPsychologyLearnabilityComputer scienceNatural language processingArtificial intelligenceCognitive psychologySpeech recognitionDevelopmental psychology

Abstract

fetched live from OpenAlex

Anagrams are frequently used by experimental psychologists interested in how the mental lexicon is organized. Until very recently, research has overlooked the importance of syllable structure in solving anagrams and assumed that solution difficulty was mainly due to frequency factors (e.g., bigram statistics). The present study uses Rasch analysis to demonstrate that the number of syllables is a very important factor influencing anagram solution difficulty for both good and poor problem solvers, with polysyllabic words being harder to solve. Furthermore, it suggests that syllable frequency may have an impact on solution times for polysyllabic words, with more frequent syllables being more difficult to solve. The study illustrates the advantages of Rasch analysis for reliable and unidimensional measurement of item difficulty.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.399
Teacher spread0.331 · 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