Exploring Self-Perceptions of Motivations in the Hospitality Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on increasing our understanding of employee motivation by applying two different but complimentary measures to unpack motivational issues in hospitality employees: the Ten Factor Model (Hersey & Blanchard, 1969; Kovach, 1987) and Alderfer’s ERG theory (Alderfer, 1972). As the third study in a longitudinal body of work, this study will surface data collected between 2000 and 2016 within the Canadian lodging industry. The value of this work is two-fold. First, it maintains the detailed characteristics of Ten Factor Model while associating it with an established needs-based motivational theory centred on basic human’s realms of existence, social, and growth needs. Second, it attempts to unpack contextual issues by exploring shifts in self-ranked motivational needs over time and, more specifically, over varied economic circumstances.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it