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Information Literacy and Science Misinformation

2020· book-chapter· en· W3008740316 on OpenAlex
Joan C. Bartlett

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

VenueAdvances in media, entertainment and the arts (AMEA) book series · 2020
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsMcGill University
Fundersnot available
KeywordsMisinformationInformation literacyProcess (computing)UnderpinningLiteracyComputer scienceInternet privacyPsychologyKnowledge managementWorld Wide WebEngineeringPedagogy

Abstract

fetched live from OpenAlex

Science and health misinformation is endemic; there can be profound consequences both for individuals and society when people make decisions based on such information. Information literacy skills provide one tool to help mitigate against misinformation. These skills include the recognition of a need for information, the ability to locate and retrieve information, and the ability to effectively use the information. Underpinning these processes are the concept of effectiveness and the ability to evaluate all steps of the process. These skills are essential if people are to be able to evaluate the sources of information, the process by which it was retrieved, and the biases inherent in its creation and dissemination. Thus, information literacy is one of tools that can be used to mitigate against misinformation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.025
Open science0.0000.000
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 head0.008
GPT teacher head0.264
Teacher spread0.257 · 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