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Record W2069205250 · doi:10.1177/0963721413495236

Validation in Reading Comprehension

2013· article· en· W2069205250 on OpenAlex
Murray Singer

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

VenueCurrent Directions in Psychological Science · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComprehensionMeaning (existential)Reading comprehensionSentenceConsistency (knowledge bases)PsychologyComputer scienceRepresentation (politics)Natural language processingTask (project management)Reading (process)LinguisticsCognitive psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Language comprehension involves analysis at the level of the word, sentence, and message and the integration of message meaning with the prior discourse and world knowledge. Contemporary research converges on another facet of comprehension: the validation of message consistency. Existing evidence already favors several principles in validation of reading and listening. Validation is initiated immediately and is routine rather than requiring intentional strategies. Successful validation is a precondition to updating the situational representation of the message. Validation applies to discourse inferences as well as explicit assertions. Finally, the memory-retrieval processes that enable validation closely resemble those of intentional discourse memory. Competing observations of people’s validation failures are proposed to systematically stem from features of the message, understander, and comprehension task. Therefore, theoretical analysis that accommodates both successful and deficient language validation ought to be attainable.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.096
GPT teacher head0.424
Teacher spread0.329 · 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