MétaCan
Menu
Back to cohort
Record W2173213633 · doi:10.5206/eei.v17i2.7605

The Writing Strategies of Post-SecondaryStudents with Writing Difficulties

2007· article· en· W2173213633 on OpenAlexaffvenue
Gina L. Harrison, Deborah Beres

Bibliographic record

VenueExceptionality Education International · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSpellingWriting processPsychologyProfessional writingMathematics educationAcademic writingSecond language writingComposition (language)Writing systemPedagogyLinguisticsSecond language

Abstract

fetched live from OpenAlex

Writing samples were examined from 42 post-secondary students with or without writing difficulties. Guided by the Simple View of Writing (Berninger et al., 2002), the samples were examined for evidence of difficulties with lower-order transcription processes and higher-order composition skills. Retrospective reports on writing strategies were also obtained. The students with writing difficulties achieved significantly lower scores across both dimensions of writing than the students without difficulties. For those with writing difficulties, strategy reports indicated an awareness of difficulties with lower-order (e.g., spelling) writing skills and an over-emphasis on these skills during the writing process, compared to the students without writing difficulties. Results are discussed in relation to the cognitive and linguistic aspects involved in skilled writing in adulthood, and the implications for accommodations and interventions for students struggling with writing at the post-secondary level.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.368
Teacher spread0.353 · 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 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

Citations12
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueExceptionality Education InternationalSame topicWriting and Handwriting EducationFrench-language works237,207