Host-Region: Safe Folklore and the Negotiation of Difference In Post-Socialist Diasporas in Newfoundland
Bibliographic record
Abstract
Many studies of diasporas focus on (large) locales where sizable diasporic populations provide room for group formation based on a single ethnicity. Scholars often treat such regions as representative of larger units, defining hostland in broad geopolitical categories of countries and even continents. Based on ethnographic research devoted to immigrants from postSocialist Europe and Asia to the Canadian island of Newfoundland, I propose the concept of host-region to emphasize a regional perspective in diaspora studies. The overall small newcomer population and the unique socio-cultural context of the island result in regionally-specific diasporic group-building dynamics, stimulating new Newfoundlanders to expand the notion of their people beyond likeminded co-ethnics. Safe home-region folklore, namely, select cultural expressions that reinforce a sense of unity and do not cause tensions within a group, offers points of connection. However, contrary to many studies that emphasize the notion of commonality within groups, I show that difference, reinforced by continuous turbulence in the home-region, can be equally important in group-building endeavors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".