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Record W2068565109 · doi:10.1145/633292.633432

Non-contractual trust, design, and human and computer interactions

2000· article· en· W2068565109 on OpenAlexaff
Elisabeth Davenport, Mark Dibben, Batya Friedman, Steven Marsh, Howard Rosenbaum, Harold Thimbleby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsComputer scienceMoralityComponent (thermodynamics)Computational trustWork (physics)Human–computer interactionHuman interactionVirtual worldKnowledge managementReputationSociologyEpistemologyEngineering

Abstract

fetched live from OpenAlex

How might trust be a component of human-computer based interaction? There are a number of dimensions involving different combinations of humans, systems and computer agents. Recent studies of trust in the workplace indicate that trust has many attributes, and that representations of trust in the virtual workplace must take account of differences in emphasis. [1] The panel members will work with a number of 'real world' scenarios that illustrate different aspects pertinent to trust and the morality of interactions

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.307
Teacher spread0.286 · 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.

Study designOther design
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

Citations5
Published2000
Admission routes1
Has abstractyes

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