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Record W2240083779 · doi:10.1177/0165551515614472

The effects of textual environment on reading comprehension: Implications for searching as learning

2016· article· en· W2240083779 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Information Science · 2016
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsInteractivityReading comprehensionReading (process)Presentation (obstetrics)Computer scienceComprehensionContext (archaeology)MultimediaHuman–computer interactionCognitive psychologyPsychologyLinguistics

Abstract

fetched live from OpenAlex

This paper reports on a study of digital reading that investigates the effects of different textual environments on information interaction and comprehension outcomes. While there is a large body of literature that compares print and digital reading, research that compares differently designed digital reading environments is limited. Such work can inform the design of information and search systems intended to support learning. This study investigated the effects of two design dimensions: Text Presentation (Plain Text vs In-Context) and Interactivity (availability of Reading Tools). Results show that the simplest textual environment (Plain Text presentation with no Interactivity) was associated with the highest comprehension outcomes, but that Interactivity mitigated the negative effects of texts presented In-Context. Both time spent reading and certain reading behaviours varied to some extent by condition and may be associated with comprehension; however, personal characteristics of the readers played little to no role in determining outcomes.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.005
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.249
Teacher spread0.236 · 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