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Record W7010584994

An investigation of the role of individual words in the comprehension of connected discourse

2013· dissertation· en· W7010584994 on OpenAlexaff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2013
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsLakehead University
Fundersnot available
KeywordsComprehensionReading comprehensionWord (group theory)Task (project management)NounReading (process)
DOInot available

Abstract

fetched live from OpenAlex

Two experiments exploring the role o individual words in reading comprehension are reported.In the first experiment one group of subjects processed text for comprehension and another attempted retention of text words.Results showed that higher comprehension level can be accompanied by lesser knowledge of text words, and increased text word familiarity by decreased comprehension of the material.The second experiment presented subjects with a series of texts in which nouns representing the topic or ''Actor" were deleted.Its results showed that subjects can successfully fill-in such words and that the group showing superior performance in this task generated a significantly higher level of text comprehension than a comparable low performance group.No differences between the high and low groups in terms of retention of random text words were found.Experimental results are discussed in relation to reading as a whole, and it is concluded that word retention and text comprehension are different processes, and that individual word quantification data may not be particularly relevant to comprehension analysis.Suggestions for further research are presented.Ill

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.236
Teacher spread0.209 · 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 teacher head, 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

Citations0
Published2013
Admission routes1
Has abstractyes

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