Bibliographic record
Abstract
This article deals with a paradox of contemporary globalization: while on the one hand, the ability to communicate over long distances allows us to further streamline and depersonalize our exchanges, on the other, there has been a rise in international events that are dedicated to professional meetings in a wide range of economic sectors since the 1980s. This article addresses the issue with a survey of two international film and TV “marketplaces” that bring together thousands of professionals in the film and television industry from around the world. By examining interactions between participants, it is emphasised that these events are above all social spaces, contrary to other accounts that reduce them to spaces for data collection or symbolic competition. By encouraging the creation of social groupings, these events indeed contribute towards delimiting a transnational professional community. Moreover, although these are commercial business events, the strictly economic stakes are greatly minimized or euphemized. The personalization of relationships between participants predominates, which is crucial in ensuring the long-term sustainability of trade.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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.
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".