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Record W4410329158 · doi:10.1080/14649365.2025.2503239

Making space for work: understanding freelancers’ hybrid work identities through work from home practices

2025· article· en· W4410329158 on OpenAlexafffundabout
Esra Alkim Karaagac, Nancy Worth

Bibliographic record

VenueSocial & Cultural Geography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWork (physics)Space (punctuation)Gig economySociologyPublic relationsEngineering ethicsPolitical scienceComputer scienceEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Through narratives of media freelancers in Toronto, Canada, this paper examines how hybrid work identities are revealed through work from home practices. For freelancers, work identities often shift between employer and employee; self-employment requires that an individual attends to both what is best for the business and what is best for the worker. This hybrid identity is naturalized in freelancing, and often hard to talk about, but it emerges through spatial strategies of working from home. We identify three scales: moving house to give more space to work or allow work to happen at home; reconfiguring home/work space, to prioritize space for work or to ensure it is comfortable and aesthetically pleasing; third, inhabiting home/work space to shift to ‘work mode’ through bodily practice. Our data consists of two sets of interviews, the first from before the start of the pandemic (conducted from December 2019 to March 2020) and the second set during the pandemic (conducted from September to December 2021). By examining the spatial strategies that freelancers enact to make work possible, we open up a way to talk about work identity, offering insights on broader back to the office debates and the changing work identities of all those able to work from home.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.100
GPT teacher head0.359
Teacher spread0.259 · 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 designTheoretical or conceptual
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

Citations1
Published2025
Admission routes3
Has abstractyes

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