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Record W4247350552 · doi:10.32920/ryerson.14647119

Do Not Disturb/Please Clean Room: The Invisible Work and Real Pain of Hotel Housekeepers in the GTA

2021· preprint· en· W4247350552 on OpenAlexaff
Sirena Liladrie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImmigrationNegotiationWork (physics)GlobalismNarrativePopulationSociologyPoliticsGender studiesPolitical scienceArtLawSocial scienceEngineering

Abstract

fetched live from OpenAlex

The hotel industry in the GTA is dependent on cheap, racialized and gendered work; the result has been significant poor health outcomes for immigrant women of colour who are over represented in this industry. This paper explores the larger structural processes intensified by neoliberal globalism that leads to the racialized segregated labour of immigrant women of colour working as hotel housekeepers. This will begin by critically analyzing the organization of the economy and the "global city" through a feminist political economy approach and by linking the downward trajectory in immigrant health to the Health Immigrant Effect and gaps in the Population Health Approach. This will be highlighted by personal narratives from immigrant women of colour currently working as housekeepers in the GTA, who have shared their stories and how they are actively contesting and negotiating with their spaces of precarious employment to promote and increase health and well being at work, in their homes and within their communities.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.972
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0140.015
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.042
GPT teacher head0.295
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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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