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Record W3176288994

Front of House Experiences within COVID-19: An analysis of a Coffee Shop in Vancouver

2021· article· en· W3176288994 on OpenAlexaboutno aff
Alyha Bardi

Bibliographic record

VenueSFU Undergraduate Research Symposium Journal · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCoronavirus disease 2019 (COVID-19)Work (physics)SAFERGovernment (linguistics)MarketingComputer scienceEngineeringMedicineComputer security
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.109
GPT teacher head0.372
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueSFU Undergraduate Research Symposium JournalSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207