Occupational Portrait of A Pandemic Workforce: Latin Americans in the Health and the Sales & Services Sectors of Canada
Bibliographic record
Abstract
Reflecting on present COVID-19 pandemic times in Canada and using both visible and ethnic ancestry information from the 2016 census, the author produced an occupational portrait of the Latin American workforce of the Health and Sales & Services sectors of the country. The focus was on full-time, full-year workers, aged 25-64, who received employment income in 2015. The workforce in the Health and Sales & Services sectors totaled 5.5 thousand and 24.3 thousand individuals respectively. The occupational portrait, which was developed based on the Canadian 2016 NOC occupational classification system, revealed an active participation of Latino workers in activities enhancing sanitary protection and the economic survival of the Canadian population. Women, and established and recent immigrants as well as those reporting Central American ethnic origins were found among those who most participated in the economic activities of the sectors. The most typical jobs performed by Latin American workers were as nursing aides in the Health sector and janitorial (males) and light or specialized cleaners (women) in the Sales & Services sector. The nature of these jobs made them a high health-risk group and vulnerable one in pandemic times as they entail working in close proximity to other colleagues and the general public.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".