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Record W7057390905

Institutions, organizations & identity : building legitimacy in the Arab Gulf

2005· dissertation· en· W7057390905 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2005
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLegitimacyInstitutionalisationInterviewLeverage (statistics)Institutional theoryIdentity (music)Organizational identityIndigenousEmerging markets
DOInot available

Abstract

fetched live from OpenAlex

Invariably, every market has a set of institutions that regulate and define economic activities. Emerging economies may be developing in a way that integrates and blends traditional practices and beliefs with international conventions and standards. In these economies, like in all others, competing logics co-exist and are used to rationalize the persistence of traditional practices and the institutionalization of reforms. The remarkable characteristic of these markets, specifically due to the increasingly globalizing business, is that they often harbour completely diverging institutional logics, including those integral to Western firms' modus operandi and those of local firms building on traditional practices. A neo-institutional theory based theoretical framework is developed to explore the interaction between society, exerting conforming pressures, and organizations, responding to these pressures. In order to explore the applicability of the developed model, a qualitative field study was conducted. Data was collected by interviewing high ranking officers of Canadian and indigenous firms operating in UAE. Among the research findings, organizational identity was found to moderate firms' strategic choices in response to institutional demands. 'Wasta', a unique local institutional artefact, was found to provide firms leverage in manipulating institutional demands

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.026
GPT teacher head0.299
Teacher spread0.273 · 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 designNot applicable
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
Published2005
Admission routes1
Has abstractyes

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