What leads to higher paying jobs in Hotel Management: Gender, qualifications or mobility?: gender, qualifications or mobility?
Bibliographic record
Abstract
EnglishThis research explored the correlation between gender, formal education, credentials, mobility, and general managers’ salaries in the Canadian hotel industry. The analysis confirmed that men are 3.85 times more likely to earn a salary greater than $90,000 than are female general managers. Managers with more frequent moves to obtain better positions were 1.3 times more likely to have a salary greater than $90,000. Completion of formal education programs or industry credentials was not correlated with such high salary levels. Research findings agree with previous literature that gender and mobility are important factors in achieving higher salary levels. However, the hypotheses related to credentials and education did not confirm the findings of previous research. This result may be due to the fact that education and credentials are related to career progression at lower levels of management or salary. This study fills a gap in the literature concerning career progression and salaries of general managers in the Canadian hotel industry portuguesEsta investigacao explorou a relacao entre genero, educacao formal, credenciais, mobilidade e os salarios dos gerentes gerais na industria canadense hotel. A analise confirmou que, nos cargos de gestao, os homens tem 3,85 vezes mais hipoteses de ter um salario maior que $90.000 do que sao as mulheres. Os gerentes com movimentos mais frequentes para obter melhores posicoes foram 1,3 vezes mais propensos a ter um salario maior que $90.000. A finalizacao de programas de educacao formal, ou credenciais da industria, nao apresentou correlacao com niveis tao altos de salario. Os resultados da pesquisa vao de encontro a literatura, em que o genero e a mobilidade sao fatores importantes para atingir os niveis salariais mais elevados. No entanto, as hipoteses relacionadas com credenciais e educacao nao confirmam os resultados de investigacoes anteriores. Este resultado pode ser devido ao facto de que a educacao e as credenciais estao relacionados com a progressao na carreira nos escaloes inferiores da administracao ou de salario. Este estudo preenche uma lacuna na literatura sobre a progressao de carreira e salarios dos gerentes gerais na industria hoteleira canadense
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.008 | 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".