Counting Migrants and Migrations: Comparing Lifetime and Fixed‐Interval Return and Onward Migration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: Lifetime measures of return and onward migration that use place of birth may be rather arbitrary, as they may not capture the essence of “home” region and therefore may not adequately represent ties to place, including where an individual grew up or went to school. The recent availability of census data that include information on place of residence five years prior to the census, one year prior, and at the time of the census allow an alternative definition of return and onward migration based upon fixed‐interval data. Employing data from the 1996 Canadian census, in this paper I first compare and examine the incidence, composition, and spatial patterns and explanations of return and onward migration through measures of lifetime and fixed‐interval data. I then suggest a typology of return migration. Findings indicate that although both measures result in similar patterns and demographic effects, fixed‐interval measures provide additional detail into the processes at work. Planned returns among younger and older adults that are most likely associated with education or employment and represent 24 percent of returns define two types of return migration. A third type is more consistent with the stereotypical image of a “failed” migration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it