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“Be in the Room Where It Happens”

2018· article· en· W2941403856 on OpenAlexfundno aff
Sheila Morrissey, John Meyer, Sushil Bhattarai

Bibliographic record

VenueBalisage series on markup technologies · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicDiverse academic research themes
Canadian institutionsnot available
FundersNuclear PhysicsNational Institute of Information and Communications TechnologyJapan Aerospace Exploration AgencyCanadian Space AgencyCenter for Long-Term Cybersecurity, University of California BerkeleyGeo-Informatics and Space Technology Development AgencyIran Telecommunication Research CenterEuropean Space AgencyAgenzia Spaziale ItalianaEuropean Organization for the Exploitation of Meteorological SatellitesKorea Aerospace Research InstituteNational Commission for Science and TechnologyTürkiye Bilimsel ve Teknolojik Araştırma KurumuNational Oceanic and Atmospheric AdministrationNational Space OrganizationChina National Space AdministrationBelgian Federal Science Policy OfficeChinese Academy of SciencesU.S. Geological SurveyIndian Space Research OrganisationCentre National d’Etudes SpatialesCommonwealth Scientific and Industrial Research OrganisationNational Aeronautics and Space Administration
KeywordsUSableVerifiable secret sharingComputer scienceKey (lock)Task (project management)Face (sociological concept)Resource (disambiguation)Digital contentInternet privacyData scienceWorld Wide WebMultimediaComputer securityEngineeringSociologySet (abstract data type)

Abstract

fetched live from OpenAlex

Institutions such as Portico that are engaged in ensuring that the digital record of our time is accessible, usable, discoverable, and verifiable for the very long term continually face the challenge of processing and managing content at very large scales, often with minimal, and sometimes diminishing, resources to accomplish the task. A key resource in meeting the challenge of preserving born-digital and digitized scholarly literature has been the NLM and JATS standards, and the community of practice centered on those standards. We will be talking about our shared experience in developing those standards: what motivated our participation, what benefits we have seen, and what challenges we still face.

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)0.005
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.724
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0060.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.165
GPT teacher head0.416
Teacher spread0.251 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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