Bibliographic record
Abstract
Scholars and policy-makers have increasingly come to realize that there is a pressing need for more comparative analysis in the study of population movements. In order to better understand the implications of migration, we need to develop analytical tools that take into account the fact that the study of a given population’s movements can always be improved upon by noting similarities and differences with analogous patterns. To build an accurate account of a given population’s experience, we have to keep in mind that there is increasingly no such thing as an isolated experience, one that is disconnected from a broader world of transnational phenomena: physically, with ease of jet travel; culturally, with global media saturation; and juridicopolitically, with interconnected national migration regimes and international bodies in place to oversee migrant flow. Perhaps the most significant aspect of the reality of migration—one that cuts across these multiple lines—is the development of transnational and cross-cultural identities, particularly those of diasporic communities in which a sense of ethnocultural belonging deeply inflects more abstract or procedural senses of belonging, such as buying into a notion of democratic citizenship. When working as scholars to build the comparative perspective, we tend to think of the international (contrasting, say, the immigration policies of the European Union with those of the United States); the geographical (interrogating the North-South divide); the economic (noting the impacts of neoliberal policy on poorer states); and the socioanthropological (comparing the attitudes and values of a diaspora with those of the home community). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".