Bibliographic record
Abstract
In a previous issue of this journal (2001a, vol. 57, no. 3), an application of reading theory to WWW documents ('hypertext') revealed two problems for beginning learners: reading an electronic format and deciphering the language used in Web pages. A third major difficulty for readers is content; it requires higher-level reading processes, which call on the reader's store of knowledge. This article proposes pedagogical solutions to these problems, including a study of Uniform Resource Locators (URLs) to enhance predictions, the analysis of visuals that support the meaning of the text, and the conversion of search engine summaries into pre-reading activities to activate schemata. Three additional skills related to content are the understanding of cultural allusions, critical reading, and the ability to read extensively. Cultural allusions are frequent in hypertext, especially in personal home pages. Readers can ask pertinent questions based on surrounding text, and they can research an allusion online to determine meaning. Critical reading is a necessary skill to develop when reading hypertext, since much information is incorrect or biased. The identification of sources and text type to gain perspective on the author's intended message is recommended. The necessity for extensive reading with the WWW requires ways of helping students choose appropriate sites and of evaluating their work in order to promote reading for meaning.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".