MétaCan
Menu
Back to cohort
Record W1509476878 · doi:10.5539/mas.v9n6p243

Evaluations of Information Assymetry

2015· article· en· W1509476878 on OpenAlexvenueno aff
Tsvetkov Victor Yakovlevich

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsObject (grammar)AsymmetryInformation flowComputer scienceSet (abstract data type)Information asymmetryInformation exchangeState (computer science)Information retrievalArtificial intelligenceAlgorithmPhysicsMicroeconomics

Abstract

fetched live from OpenAlex

This paper is the analytical work describing evaluation methods of some types of information asymmetry. Duality of possible evaluations of information asymmetry is shown: in comparison to the similar object and the set goal. The types of informational situations where asymmetry occurs are described. The types of information asymmetry are described: on information, on information exchange, on information interaction, on information situation, on information flow. The ideas of the general state of object and the informational state of object are described. The difference between them is shown. The concept of awareness of the object or subject is given. The qualitative and quantitative evaluation methods of information asymmetry are given. It is shown that the asymmetry is possible not only on the state of object, but also on information flow coming to the objects. The limitations and possible negative consequences of qualitative estimates are shown. These analytical terms allow defining the informational characteristics of objects such as:

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.049
GPT teacher head0.304
Teacher spread0.255 · 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 designSimulation or modeling
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

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicCognitive Science and MappingFrench-language works237,207