Trapped in a Cycle of Low Expectations: An Exploration of High School Seniors' Perspectives About Academic Reading
Bibliographic record
Abstract
Reports show that the reading proficiency scores for 17-year-olds have stagnated over the past several decades. In this study, the authors explored older students' academic reading perceptions that might suggest links to proficiency. What do high school seniors really think about class reading? Do they understand what they read? How do they view teacher support for content reading? A quarter of the senior class of one mid-sized high school responded to open-ended questions such as these as well as a Likert-style reading attitude survey. Additionally, the teachers of the student study sample were interviewed about their students' reading behaviors and attitudes. Data revealed that these seniors largely felt confident in their class reading abilities despite the fact that most said they did not do much reading either for school or recreationally. Seniors also reported a lower tolerance for reading long periods of time and showed little preference for reading informational texts. Yet most participants planned to go to college and felt positively about the challenges presented by college-level reading. Student and teacher reports suggested both parties may be locked in a reciprocating cycle of low reading expectations that maintain student non-reading behaviors and unrealistic ideas about the skill level necessary for informational reading comprehension.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".