With Davos in Full Swing, How Likely Are We To See Leaders Cooperate?
Bibliographic record
Abstract
With the great and the good gathering in Davos again, the relative lack of snow must at least focus all on the task ahead. Yes, much of the discussions are taken up by the war in Ukraine but delegates also know that a lot of work is needed to overcome the doom and gloom that seems to be affecting participants. Chinese data just released suggests that despite a better 4th quarter growth in China than anticipated, the economy slowed down markedly in 2022 as a whole to just 3%, There are also worries about future growth trends as the Chinese population has also started to shrink despite the reversal in 2015 of its ‘one child’ policy. Slower growth in China of course affects the many countries in Asia which trade with China and increases concerns about supply chain issues as the country now struggles with the spread of Covid after its abandonment of its zero-covid policy at the turn of the year.
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 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".