Bibliographic record
Abstract
Unemployment rates are a useful measure when looking at the functioning of the labour market, and are an important economic indicator. In this study, data was collected from the Canadian Labour Force Survey (LFS) in order to model Alberta’s monthly unemployment rate using time series analysis. The seasonally adjusted monthly unemployment rate in Alberta from January 1976 to November 2020 was analyzed as a time-series in order to fit an appropriate model. Models were chosen based on several evaluative methods, including checking normality, stationarity, performing outlier analysis, and generating the ACF, PACF, and EACF of the time-series. Two models were chosen and compared to determine the best model fit. The most effective of these models could be used to better understand Alberta’s unemployment rates over time and could be maintained to maximize accuracy and longevity. Additionally, several potential explanations for changes in unemployment rates were explored. For further research, examining the impact of the COVID-19 pandemic in hindsight, as well as comparative analysis between provinces, could be valuable in order to evaluate economic resilience and to better understand the factors affecting unemployment across Canada. Department: Statistics Faculty Mentor: Dr. Cristina Anton
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".