Safeguarding Hospitality Service When the Unexpected Happens: Lessons Learned from the Blackout of ‘03
Bibliographic record
Abstract
The blackout of '03 took many hoteliers in the northeastern United States and Canada largely by surprise. Hotel managers found themselves scrambling to serve guests overnight in darkened hotels, many of which did not have running water, let alone expected amenities. Despite the challenges, hoteliers responding to a postblackout survey reported that their staff members were up to the task of providing hospitality for guests—often by devising creative processes for check-in and checkout, food service, and the like. For their part, guests were mostly understanding about the power failure and appreciated hotel employees' efforts on their behalf. However, guests were surprised that hotels often did not have backup power to maintain critical systems after emergency power failed. Service quality and the guest experience typically suffered at those hotels that lost power and were ill prepared to deal with disruptions in the service system. This article examines these problems and provides insights for how to safeguard service when the unexpected happens.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".