Front of House Experiences within COVID-19: An analysis of a Coffee Shop in Vancouver
Bibliographic record
Abstract
Front of House (FOH) Coffee Shop employees in the COVID-19 pandemic, by nature of their work, are required to share indoor spaces in close proximity to customers and co-workers, who could be potential carriers of COVID-19. Due to the possibility of this spread, safety measures have been implemented on the scales of the BC government, coffee shop chains, and individual coffee shops (within chains), to hinder and prevent the spread of COVID-19. This presentation will delve into the corresponding effects of these implemented safety measures on FOH workers regarding their workloads, and interactions with customers, within a single cafe in Vancouver.Findings from changing customer interactions due to COVID safety measures, will be discussed as impacting the work of baristas in terms of (1) prolonging customer-by-customer interactions, (2) elevating the status and power of workers through BC mandates, and (3) increasing the emotional workloads of workers.Impacted workloads will also be discussed in terms of increased cleaning and sanitation practices, and fluctuations in cafe busyness, due to safer-at-home orders. Key findings encompass the following:1. The pandemic has caused an overall trend of lengthening customer-by-customer interactions, due to losses of customer freedoms within the cafe.2. During periods of busyness, the workloads of FOH coffee workers has increased compared to pre-COVID, making rushes more exhausting and stressful.3. Fluctuations regarding cafe busyness and, lengths of customer interactions, has made the work of these workers less stable, and more unpredictable. Making their workspaces a place of constant change and adjustment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".