Tallin International Drawing Triennial Award Winner:Award Winner
Bibliographic record
Abstract
The 6th Tallinn Drawing Triennial “Impulse” will be inaugurated on 9th July at 5 PM in ARS Art Factory. Exhibitions will be held in the ARS Project Space, seminar hall and gallery no. 112. 85 participanting artists represent 21 countries across the globe.<br/><br/>On 9th July at 1 PM a conference will be held (in Estonian), with presentations by Britta Benno, Lembe Ruben, Mai Levin, Margot Kask, Maria-Kristiina Ulas, Elnara Taidre, Marija Marcelionyte-Paliuke, Loit Jõekalda On 9th July at 7 PM a drawing performance “Shadow catcher” by Ülle Marks (accompanied by acrobat Ireen Peegel) will take place in the courtyard of ARS, producing a large-format weather-proof art work.<br/><br/><br/>In the courtyard of ARS further events will take place during the exhibition period: on 10th July at 1 PM Maria-Kristiina Ulas, on 17th July at 1 PM Jaanika Peerna. The exhibition will remain open until 31st July, from Monday to Friday 12–6 PM, on Saturday 12–4 PM.<br/><br/> <br/><br/>The Drawing Triennial is supported by Tallinn Culture and Sports Department, Cultural Endowment of Estonia, Estonian National Culture Foundation, Estonian Artists' Association, Association of Estonian Printmakers. The conceptualizing text of the triennial was written by Lembe Ruben. The coordinator of the triennial is Loit Jõekalda.<br/><br/> <br/><br/>Participating artists:<br/><br/>Anna Gaya Nidzgorska (Austria), Endi Poskovic (Bosnia and Herzegovina), Laura Ibanez Lopez and Juan Perdiguero (Spain), Ad Stijman (Netherlands), Ana Vivoda (Croatia), Giulia Lanza (Italy), Ahmed Modhir (Iraq), Naoji Ishiyama (Japan), Derek Besant and Alexandra Haeseker (Canada), Klavs Upaciers (Latvia), Marija Marcelionyte-Paliuke (Lithuania), Anki King (Norway), Krzysztof Molenda and Mateusz Otreba (Poland), Luis Filipe Rodrigues (Portugal), Julien Morel and Mark Tanguy (France), Katharina Lewonig Vita and Tobias Stengel (Germany), Blazej Balaz and Maria Balazova (Slovakia), Kaisa Koljonen and Paula Suominen (Finland), Paul Coldwell and Johanna Love (UK), Marieken Cochius and Kalev Peets (USA), Vassili Bertels (Russia), as well as Jüri Arrak, Evelyn Grzinich, Albert Gulk, Ulvi Haagensen, Hannah Harkes, Inga Heamägi, Maie Helm, Jarõna Ilo, Kristiina Jakimenko, Virge Jõekalda, Ott Kadarik, Erki Kannus, Jüri Kask, Jüri Kass, Tiiu Kirsipuu, Angela Soop, Ilmar Kruusamäe, Leena Kuutma, Tiia Külv, Tarrvi Laamann, Heikki Leis, Anna Litvinova, Ülle Marks, Johanna Mudist, Katariin Mudist, Naima Neidre, Mall Nukke, Maarja Nurk, Enno Ootsing, Kristina Paabus, Kadi Pajupuu, Illimar Paul, Katrin Paumer, Jaanika Peerna, Katrin Piile, Mark-Antonius Puhkan, Anu Purre, Matti Pärk, Uno Roosvalt, Reti Saks, Feliks Sarv, Pavel Semjonov, Maria Sidljarevitš, Sandra Sirp, Katri Smitt, Kärt Summatavet, Kuzja Zverev, Siiri Taimla, Maria Kristiina Ulas, Aili Vahtrapuu, Katrin Valgemäe, Kelli Valk, Maara Vint, Marje Üksine (all from Estonia). <br/>
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".