Bibliographic record
Abstract
A Moveable Feast, this is the definition Hemingway gave to Paris, a big city where he spent a happy period of his life. A celebration city, a city and a celebration, celebration spaces have become the subject of the issue. And a winter city – for those cities which have got winter anyway. In Irkutsk, this topic seems to be seriously considered (p.75). In Moscow, the already traditional Journey to Christmas has lasted for the three winter months, although without natural attributes such as snow, frost and other unnecessary stuff. Meanwhile, climate change made the International Union of Architects join its efforts with the UNO and publish the Declaration (p. 5). Anyway, the luxury of the decorated center made our capital the leader among European brothers and sisters. As for the competition among cities, do non-capitals with their modest budgets have any chance to hit the podium? “They do”, says the FORBES LIFE and publishes the list of top 10 most promising cities in Russia, which includes Irkutsk. Among other things, it is due to the Winter University (its XXIst session will be featured in the next issue) and a successful Quarter 130 (see the details that have not been published before on pp. 62-69). Italian cities of Siena, Berga, Assisi and Venice can be truly called celebration cities. Cities give birth to celebrations, but a celebration can also inspire the city to start a new life. The heroes of this issue are Perm, Novosibirsk, Norilsk, Irkutsk, Ulan Bator, Dresden and Kyakhta. The city declared the World Capital of Architecture for 2020, Rio de Janeiro (pp. 6-7) will host us for the UIA Congress in July.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.021 |
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 source (direct Gemma or distilled Codex), 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".