MétaCan
Menu
Back to cohort

Exploring and depicting information pathologies

2024· article· en· W4403063191 on OpenAlexvenueno aff
Paulo Nuno Vicente

Bibliographic record

VenueCanadian Journal of Information and Library Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceData science

Abstract

fetched live from OpenAlex

This study addresses two information pathologies: information technology anxiety (ITA) and information overload (IO). This research aims to determine the level of both ITA and IO amongst the undergraduate students of Information Science of the Faculty of Arts and Humanities of the University of Coimbra, Portugal, and to ascertain how both phenomena correlate with students’ gender, age, number of enrolments in the degree and average frequency of use of the university’s libraries throughout the academic year. Concerning the research methodology, we opted for exploratory and descriptive research with a quantitative approach and adopted the case study and questionnaire survey methods. Both descriptive and inferential statistics were used as methods of data analysis. The findings, concerning 39\% of the population under study (N = 88; n = 34), reveal a considerably low prevalence of ITA but a substantial occurrence of IO. Both ITA and IO correlate positively with the female gender. Students aged 19 and 24 years are the least likely to manifest both ITA and IO; in turn, students aged 22 years are the most likely to simultaneously show ITA and IO. Both phenomena tend to increase as students progress through the degree. Last, a higher frequency of use of the university’s libraries is associated with a higher level of ITA and IO. Our findings emphasize the high incidence of IO in university students and the need for increased skills in all dimensions of the human-information-technology relationship/system, particularly in the information filtering and management areas.

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 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 categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.079
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 head0.058
GPT teacher head0.258
Teacher spread0.200 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Information and Library ScienceSame topicMisinformation and Its ImpactsFrench-language works237,207