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Record W2970362787 · doi:10.4038/jula.v22i2.7939

Perception of Academics Regarding the Credibility of Online Resources: Open Access versus Subscribed Journals with Special Reference to Eastern University, Sri Lanka

2019· article· en· W2970362787 on OpenAlexaff
M Ravikumar, T. Ramanan

Bibliographic record

VenueJournal of the University Librarians Association of Sri Lanka · 2019
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCredibilityVisibilityPerceptionPublic relationsThe InternetPublishingWork (physics)Open dataInternet accessSri lankaPolitical scienceInternet privacyBusinessComputer scienceWorld Wide WebSociologyPsychologyGeographyTanzaniaLawEngineeringSocioeconomics

Abstract

fetched live from OpenAlex

Electronic information resources can be either credible or unreliable. Vast majority of information on the Internet is neither authenticated nor having any mechanism to validate its credibility. Thus, it has been a great challenge for any researcher who uses them for their problem-solving. Education and research institutions use and contribute to the development of academic journals that broadly fall under two categories, namely subscribed and open access. In this regard, this paper discusses about the views of academics of Eastern University, Sri Lanka depending on these resources for using and publishing their research work. Particularly, this study investigates about the perception of academics over subscription journals and open access resources in regard to their accessibility, visibility and credibility. Summary of results show that researchers hold high value for online subscribed journals in terms of their credibility, meanwhile accessibility to the same is quite a challenge. On the other hand, open access journals give rise to visibility and affordability. Therefore, authors shed light on the need of making open access resources more credible.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.198
Threshold uncertainty score0.748

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0040.001
Research integrity0.0000.001
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 head0.056
GPT teacher head0.292
Teacher spread0.237 · 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 teacher head, 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

Citations2
Published2019
Admission routes1
Has abstractyes

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