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Record W7103137407 · doi:10.5281/zenodo.17491714

Contouren Open Science Infrastructuur

2025· article· nl· W7103137407 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languagenl
FieldBusiness, Management and Accounting
TopicOptics and Image Analysis
Canadian institutionsCanarie
Fundersnot available
KeywordsOpen universityPlan (archaeology)Open science

Abstract

fetched live from OpenAlex

Het doel van het Strategisch Plan Integrale Infrastructuur Open Science (SPII) is het ontwikkelen van een plan voor een samenhangende, federatieve infrastructuur die onderzoekers en instellingen ondersteunt bij het faciliteren van Open Science. De aanleiding is het ontbreken van een gemeenschappelijk kader voor een Open Science Infrastructuur (OSI), hetgeen het risico van versnippering met zich meebrengt. SPII beoogt een gemeenschappelijk gedragen streefbeeld en streefarchitectuur te ontwikkelen dat een sturende en harmoniserende werking heeft zodat er stapsgewijs een adequate, landelijke Open Science Infrastructuur kan worden gerealiseerd. In dit project is SPII Deel 1 ontwikkeld met een visie, een kader voor de infrastructuur en een fit-gap analyse t.o.v. de bestaande situatie. Wanneer SPII Deel 1 omarmd wordt, kan dit in SPII Deel 2 en 3 verder uitgewerkt worden.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0040.004
Scholarly communication0.0120.010
Open science0.0020.012
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0460.010

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 head0.023
GPT teacher head0.262
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

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
Published2025
Admission routes1
Has abstractyes

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