Bibliographic record
Abstract
Culture is a nuanced term that is hard to define conclusively. It is largely used to refer to the totality of values, basic assumptions and life orientations, beliefs, policies, procedures, and codes of conduct. These are shared by a group of people and affect their behavior and the way they lead their lives. Cultural Analytics (CA), as a field, examines vast amounts of cultural data (books, images, newspapers, music, literature, etc.) to derive culturally relevant insights. Various methods and techniques (natural language processing, network analysis, visualization, and data mining) are applied to cultural components so that they are useful for research in the humanities. CA can help identify the behavioral components of human cultures and provide an accurate insight into the degree to which people conform to the current or target culture. It utilizes the corpora, metadata, and tools of text and image analysis to provide meaningful insights into the subject of research. This special issue explores how we can improve current performance in terms of understanding Arab cultural and social change and how we can implement behavioral changes or transformations more smoothly. The papers in this special issue use digitized printed texts, including books, journals, and printed ephemera to expand our knowledge of the key players in the printed world during a crucial period of modern Arab history. The combination of these sources will illuminate the intellectual trajectories of those who produced such texts and their transnational formal and informal networks.
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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.031 | 0.056 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".