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Record W2123734257 · doi:10.1177/0001839215589813

Place and Institutional Work

2015· article· en· W2123734257 on OpenAlexaboutno aff
Thomas B. Lawrence, Graham Dover

Bibliographic record

VenueAdministrative Science Quarterly · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)AccommodationContext (archaeology)SociologyPublic relationsQualitative researchProcess (computing)Political scienceSocial scienceGeographyPsychology

Abstract

fetched live from OpenAlex

The places in which organizational life occurs can have profound impacts on actors, actions, and outcomes but are largely ignored in organizational research. Drawing on ideas from social geography, we explore the roles that places play in institutional work. The context for our study is the domain of housing for the hard-to-house, within which we conducted two qualitative case studies: the establishment of Canada’s first residential and day-care facility for people living with HIV/AIDS, and the creation of a municipal program to provide temporary overnight accommodation for homeless people in local churches. In examining these cases, we found that places played three key roles: places contained, mediated, and complicated institutional work. Each of these roles was associated with a distinct ontology of place: places as social enclosures, as signifiers, and as practical objects. Our findings have significant implications for how we understand the relationship between location and organizations and allow us to develop a process model of places, institutions, and institutional work.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.045
Scholarly communication0.0100.006
Open science0.0010.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.048
GPT teacher head0.273
Teacher spread0.225 · 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 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

Citations199
Published2015
Admission routes1
Has abstractyes

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