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Record W3161881634 · doi:10.69554/escu3448

Recognising the socio-technical opportunity of workplace : An analysis of early responses to COVID-19

2020· article· en· W3161881634 on OpenAlexaff
Chris Moriarty, Matthew Tucker, Ian Ellison, James Pinder, Hannah Wilson

Bibliographic record

VenueCorporate real estate journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Economy and Work Transformation
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SociologyPsychologyGeographyVirologyMedicineOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19 has disrupted the ways in which we work, offering an opportunity to rethink our workplaces. Organisations have had to adapt and respond in unprecedented ways to enable continued organisational performance that have come to see many people working from home. Early responses to ‘return-to-work’ have sought to repurpose existing workspace arrangements, but they miss the unique opportunity to reconceive ‘workplace’ more comprehensively, as well as the role the property community has in enabling work. This paper aims to highlight the opportunity of viewing workplace holistically through the lens of socio-technical systems. An examination of the early responses to the pandemic identified a focus on the technical aspects of reoccupying workspaces, but taking from socio-technical systems, this should not be to the detriment of other factors. A more nuanced debate regarding who should return to work and how this will occur is presented, which highlights further a need to move beyond the physical workspace and to reflect on how we can enable ways of working.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.149
GPT teacher head0.357
Teacher spread0.207 · 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.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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