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Record W2014510465 · doi:10.1075/ni.15.2.04mck

Improving story complexity and cohesion

2005· article· en· W2014510465 on OpenAlexaff
Anne McKeough, Lynn Davis, Nicole M. Forgeron, Anthony Marini, Tak Fung

Bibliographic record

VenueNarrative Inquiry · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCohesion (chemistry)NarrativeGroup cohesivenessPsychologyCoherence (philosophical gambling strategy)CognitionMathematics educationCognitive psychologyComputer scienceSocial psychologyLinguisticsMathematics

Abstract

fetched live from OpenAlex

The aim of the present study was to analyze the relative effectiveness of two first grade instruction programs: a developmental program that focused on the structural and social-psychological components of stories and their cohesion and a process oriented approach. A total of 43 children participated in daily sessions over 3 months (experimental group N = 22, comparison group N = 21). Measures of conceptual language and oral narrative were obtained and participants' protocols were analyzed for plot and coherence. Statistical analyses showed that the developmental method was more effective than the process approach in advancing the complexity and cohesion of children's narratives. To explore the interactions between instruction and learning, a time series analysis was conducted with seven randomly selected experimental group participants. These results showed that gains did not follow a linear pattern and that performance was shaped by the cognitive complexity of task demands. Implications for the development of narrative thought and classroom instruction are discussed. (Narrative, Instruction, Development)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.101
GPT teacher head0.384
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations20
Published2005
Admission routes1
Has abstractyes

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