Journal of Human Security Research Article Educational Pathways to Remote Employment in Isolated Communities
Bibliographic record
Abstract
Abstract: Those who live in isolated communities often lack reliable, skilled employment opportunities, which fundamentally undermines their human security. For individuals who wish to remain in their isolated communities for family, religious, philosophical or other reasons, their attachment to their communities creates a disincentive for higher education. This promotes low educational achievement, which in turn results in low socioeconomic status, lack of social mobility, and a generational cycle of poverty. The hu-man misery that results from such a feedback loop is observed in isolated communities throughout North America, including aboriginal communities in Canada. Fortunately, maturation of information and commu-nication technologies now offers individuals the potential to gain high-skilled employment while living in an isolated community, using both (i) virtual work/remote work and (ii) remote training and education. To examine that potential, this study: 1) categorizes high-skill careers that demand a higher education and are widely viable for remote work, 2) examines options for obtaining the required education remotely, and 3) performs an economic analysis of investing in remote education, quantifying the results in return on investment. The results show that the Internet has now opened up the possibility of both remote education and remote work. Though the investment in college education is significant, there are loans available and
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.084 | 0.003 |
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".