Bibliographic record
Abstract
Globalization is a hot topic of the day. Not a day goes by without us being overwhelmed by the overwhelming news resulting from this all-out integration: relocation of jobs to countries where working conditions are too often unacceptable, closures of companies due to overly intense global competition, deterioration of the environment resulting from unbridled production, etc. In addition, workers’ worries about the consequences of China’s growth in the world of work are added. In this age where finance is queen and dictates the behavior of business leaders, where companies close their doors even if their business is good, where national champions are bought without the governments do not lift a finger, where Savers are robbed by unscrupulous financial actors who remain unpunished, the world seems more uncertain than ever. Recently, the world seems to have entered an era of great turbulence: unprecedented financial scandals which in their wake lead to many job losses; real estate and financial crises in the United States that threaten the balance of the global economy; rising food prices and violent protests in several developing countries. And international institutions, supposed to manage these crises, seem powerless to contain them.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".