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Record W4312985528 · doi:10.2139/ssrn.4222512

International Law Association Committee on Participation in Global Cultural Heritage Governance - Executive Summary of Final Report (2022) (German)

2022· article· en· W4312985528 on OpenAlexaff
Andrzej Jakubowski, Lucas Lixinski, Thomas Adlercreutz, Marina Lostal, Kaare Bangert, Fernando Loureiro Bastos, Janet Blake, Nudrat Majeed, Aïda Tamer Chammas, Arshad Ghaffar, Clementine Bories, James A. R. Nafziger, Marine They, Victoria R. Nalule, Irene Calboli, Robert K. Paterson, Rodrigo Carlos Cespedes, Robert Peters, Kalliopi Chainoglou, Alexander Carl Dinopoulos, Kevin R. Chamberlain, Eleni Polymenopoulou, Patricia Conlan, Elvira Prado Alegre, Amy Strecker, Beatriz Barreiro Carril, Marie Cornu, Piers Davies, Astrid Reisinger Coracini, Marie Sophie de Clippele, Marc-André Renold, Yvonne Donders, Alessandro Chechi, Evelien Campfens, Alison Dundes Renteln, Craig Forrest, Aziz Tuffi Saliba, Nicholas Augustinos, Alice Lopes Fabris, Marcilio Toscano Franca‐Filho, Jorge Sanchez Cordero Davila, Manlio Frigo, Sebastián Green Martínez, Benedetta Ubertazzi, Louis van Wyk, Kristin Hausler, Sabine von Schorlemer, Toshiyuki Kono, Yoshiaki Sato, Ana Filipa Vrdoljak, Gyooho Lee, Jie Huang

Bibliographic record

VenueSSRN Electronic Journal · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGermanExecutive committeeCorporate governanceAssociation (psychology)Political scienceLawCultural heritageExecutive boardAccountingPublic administrationManagementPsychologyBusinessGeographyEconomics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.023
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.112
Threshold uncertainty score0.222

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.029
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.008
Science and technology studies0.0060.002
Scholarly communication0.0150.007
Open science0.0060.006
Research integrity0.0320.015
Insufficient payload (model declined to judge)0.0480.027

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 head0.017
GPT teacher head0.294
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractno

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