Bibliographic record
Abstract
The government of Prince Edward Island (PEI) has embarked on a strategy that links economic growth and prosperity to population growth, with immigration as a key component. Now the Department of Workforce and Advanced Learning wishes to target Islanders who have lived on PEI previously but now live elsewhere. In order to develop effective, evidence-based policy to encourage their repatriation, it is important to better understand why they moved away, and what they see as the opportunities and barriers to returning. In early 2018, the Institute of Island Studies (IIS) designed and administered a survey in partnership with the Department. Consisting of 26 questions, a link was sent electronically to alumni from PEI post-secondary institutions; posted to the WorkPEI Facebook page; and included in the IIS’s newsletter. A total of 683 respondees painted a picture of why they left and what it would take to come back, with results ranging from education and jobs to lifestyle and family. This research has implications for rural communities: how can the data be used to help build healthy and prosperous communities? This paper explores some of the findings and follow-up in developing evidence-based policy that supports repatriation as an economic development strategy.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".