Causal Attribution Profiles as a Function of Reading Skills, Hyperactivity, and Inattention
Bibliographic record
Abstract
The causes that individuals attribute to reading outcomes shape future behaviors, including engagement or persistence with learning tasks. Although previous reading motivation research has examined differences between typical and struggling readers, there may be unique dynamics related to varying levels of reading and attention skills. Using latent profile analysis, we found 4 groups informed by internal attributions to ability and effort. Reading skills, inattention, and hyperactivity/impulsivity were investigated as functional correlates of attribution profiles. Participants were 1,312 youth (8-15 years of age) of predominantly African American and Hispanic racial/ethnic heritage. More adaptive attribution profiles had greater reading performance and lower inattention. The reverse was found for the least adaptive profile with associations to greater reading and attention difficulties. Distinct attribution profiles also existed across similar-achieving groups. Understanding reading-related attributions may inform instructional efforts in reading. Promoting adaptive attributions may foster engagement with texts despite learning difficulties and, in turn, support reading achievement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".