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Record W4322503354 · doi:10.1111/lit.12315

The roots of reading for pleasure: Recollections of reading and current habits

2023· article· en· W4322503354 on OpenAlexafffund
Manzar Zare, Stephanie Kozak, Monyka L. Rodrigues, Sandra Martin‐Chang

Bibliographic record

VenueLiteracy · 2023
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReading (process)PleasurePsychologyLiteracyDevelopmental psychologyTest (biology)PopulationPedagogyLinguisticsMedicine

Abstract

fetched live from OpenAlex

Abstract Children's early literacy experiences are critical, yet it remains unclear whether memories of early reading instruction continue to be associated with reading habits into adulthood. We examined the association between recollections of reading experiences and present‐day reading habits in an adult population. University students responded in writing to three open‐ended prompts asking about their memories of reading during early childhood, elementary school and high school. They also completed two questionnaires inquiring about reading enjoyment and frequency in elementary school and high school. For the concurrent measures of reading, participants described their current reading habits in an open‐ended prompt and completed an author recognition test. Results showed positive links between favourable memories of reading during elementary and high school years and present‐day reading habits. Conversely, unfavourable memories during high school were associated with unenthusiastic present‐day reading habits. We found that reading instruction in school forms long‐lasting memories, and these memories are linked in meaningful ways with print exposure during adulthood.

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)0.007
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
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.360
Teacher spread0.325 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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