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Record W1921791646 · doi:10.47678/cjhe.v32i3.183418

Stumped by Headlines: Investigating a Functional Knowledge Deficit

2002· article· en· W1921791646 on OpenAlexaffvenue
Sheldon Ungar

Bibliographic record

VenueCanadian Journal of Higher Education · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsIgnoranceNexus (standard)NewspaperReading (process)Scale (ratio)PsychologyKnowledge economyPositive economicsEpistemologySociologyPolitical scienceKnowledge managementComputer scienceEconomicsMedia studiesLaw

Abstract

fetched live from OpenAlex

Competing claims about the level of ignorance, or knowledge, among the current Nexus generation are addressed. The core of the paper is a theoretical analysis of ignorance in the knowledge society. Specifically, the knowledge-ignorance paradox suggests that the intense specialization demanded by a knowledge economy militates against a broader information society and gives rise to "reading reluctance." To provide evidence for this analysis, the results of a small-scale study testing the idea of a "functional knowledge deficit" are presented. Students were asked to identify metaphorical terms that are commonly used without definition in newspaper captions. The results revealed that students could only identify about 30% of these common expressions, and that they did not do better with terms derived from computers or the popular culture. Significant differences were also found between male and female responses. Both the implications of the findings and further avenues of research are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0010.005
Scholarly communication0.0030.007
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.071
GPT teacher head0.314
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2002
Admission routes2
Has abstractyes

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