Supplemental Materials: Instruction Increases Canadian Students’ Preference for and Use of Lateral Reading Strategies to Fact-check Online Information
Bibliographic record
Abstract
This repository contains the CTRL-F curriculum materials, assessments, analysis code and output, appendix, and supplemental tables for our paper reporting findings from a large-scale effort to provide lateral reading instruction to Canadian middle school and high school students in Fall 2020. The data used for the analysis are not publicly available. The abstract for our paper, entitled "Instruction Increases Canadian Students’ Preference for and Use of Lateral Reading Strategies to Fact-check Online Information", may be found below: "Canadian middle and high school students (N = 2,278) completed a “CTRL-F” curriculum teaching them how to evaluate online information by reading laterally to investigate sources, check claims, and trace information to original contexts. A subset of CTRL-F students (N = 316) were in classes with teacher-matched control groups (N = 287). Some CTRL-F students (N = 994) completed a delayed posttest. At pretest, students indicated preference for some lateral reading strategies, but preference rarely translated into use. Following instruction, CTRL-F students showed greater preference for and use of lateral reading than controls, and greater alignment between preference and use. The curriculum’s impact varied by demographic factors, but not by differences in implementation. Gains were maintained from posttest to delayed posttest. Direct instruction and practice in lateral reading appear to strengthen connections between students’ preferences and utilization of these strategies to evaluate online content relevant to academic and personal life."
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 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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.493 | 0.053 |
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".