MétaCan
Menu
Back to cohort
Record W4285128742 · doi:10.18653/v1/2022.computel-1

Proceedings of the Fifth Workshop on the Use of Computational Methods in the Study of Endangered Languages

2022· paratext· en· W4285128742 on OpenAlexaff
Sarah Moeller, Antonios Anastasopoulos, Aditi Chaudhary, Atticus Harrigan, Alexis Palmer, Alexandre Arkhipov, Alexis Michaud, Anna Kazantseva, Borini Lahiri, Christopher Cox, Claire Bowern, Daan van Esch, Dorothee Beermann, Elizabeth Salesky, Emily M. Bender, Emily Prud’hommeaux, Francis M. Tyers, František Kratochvíl, Gary Simons, Jean Maillard, Facebook Ai, Jeffrey Good, Judith L. Klavans, Luke Gessler, Martin Benjamin, Olga Lovick, Paul Trilsbeek, Richard Sproat, Ritesh Kumar, Robert Forkel, Sonal Sinha, Steven Bird, Ivan Ubaleht, Taisto-Kalevi Raudalainen, Bill Dyer, Roberto Zariquiey, Arturo Oncevay, Javier Enrique Díaz Vera, Gina‐Anne Levow, Patrick Lit- Tell, Kristen Howell, Manuel Mager, Abteen Ebrahimi, John E. Ortega, Annette Rios, Angela Fan, Xi- Mena Gutierrez-Vasques, Luis Chiruzzo, Gustavo A. Giménez-Lugo, Ricardo Ramos, Vladimir Ivan, Meza Ruiz, Rolando Coto‐Solano, Arya D. McCarthy, Christo Kirov, Matteo Grella, Amrit Nidhi, Patrick Xia, Kyle Gorman, Ekate- Rina Vylomova, Sabrina J. Mielke, Miikka Silfverberg, Timofey Arkhangelskiy, Na- Taly Krizhanovsky, Andrew Krizhanovsky, Elena Klyachko, Alexey Sorokin, John Mansfield, Valts Ernštreits, Yuval Pinter, Cassandra L. Jacobs, Ryan Cotterell, Mans Hulden, Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, Timi Fasubaa, Taiwo Fagbohungbe, Solomon Oluwole Akinola, Shamsuddeen Muham- Mad, Salomon Kabongo Kabenamualu, Salomey Osei, Freshia Sackey, Rubungo Andre Niyongabo, Ricky Macharm, Perez Ogayo, Orevaoghene Ahia, Meressa Berhe, Mofetoluwa Adeyemi, Masabata Mokgesi-Selinga, Lawrence Okegbemi, Laura Martinus, Kolawole Tajudeen, Kevin Degila, Kelechi Ogueji, Kathleen Siminyu, Julia Kreutzer, Jason Webster, Tayyaba Nusrat Ali, Jade Abbott, Iroro Orife, Ignatius Ezeani, Idris Abdulkadir Dangana, Herman Kamper, Hady Elsahar, Good- Ness Duru, Ghollah Kioko, Murhabazi Espoir, Elan van Biljon, Daniel Whitenack, Tiago Pimentel, Maria Ryskina, Shijie Wu, Eleanor Chodroff, Brian Leonard, Gar- Rett Nicolai, Yustinus Ghanggo Ate, Khal- Ifa Salam, Nizar Habash, Charbel El-Khaissi, Omer Gold- Man, Michael Gasser, W.A. Lane, Matt Coler, Jaime Rafael, Montoya Samame, Gema Celeste, Silva Villegas, David Guriel, Jean- Philippe Bernardy, Andrey Shcherbakov, Аziyana V. Bayyr-ool, Karina Sheifer, Sofya Ganieva, Matvey Plugaryov, Ali Salehi, Natalia Krizhanovsky, Clara Va- Nia, Sardana Ivanova, Aelita Salchak, Christo- Pher Straughn, Zoey Liu, Jonathan North, Duygu Ataman, Witold Kieraś, Marcin Woliński, Totok Suhardijanto, Niklas Stoehr, Zahroh Nuriah, Shyam Ratan, Edoardo Maria Ponti, Grant Aiton, Richard Hatcher, Botond Barta, Dorina Lakatos, Gábor Szolnok, Farhan Samir

Bibliographic record

Venuenot available
Typeparatext
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsCarleton UniversityFirst Nations University of CanadaUniversity of SaskatchewanUniversity of AlbertaNational Research Council CanadaUniversity of British Columbia
FundersConsejo Nacional de Ciencia, Tecnología e Innovación Tecnológica
KeywordsEndangered speciesComputer scienceProgramming languageEcologyBiology

Abstract

fetched live from OpenAlex

In this paper we present the speech corpus for the Siberian Ingrian Finnish language. The speech corpus includes: audio data, annotations, software tools for data processing, two databases and a web application. We have published part of the audio data and annotations. The software tool for parsing annotation files and feeding a relational database is developed and published under a free license. A web application is developed and available. At this moment, about 300 words and 200 phrases can be displayed using this web application.

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)0.015
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0080.005
Open science0.0030.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0210.004

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 head0.077
GPT teacher head0.384
Teacher spread0.307 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicNatural Language Processing TechniquesFrench-language works237,207